diff --git a/README.md b/README.md
index 23c8abd0..2fd1f0b6 100644
--- a/README.md
+++ b/README.md
@@ -152,7 +152,7 @@ ts.load_series(utils.search_path("eeg-alcohol"))
 ts.normalize(normalizer="z_score")
 
 # plot a subset of time series
-ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap/assets")
+ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets")
 
 # print a subset of time series
 ts.print(nbr_series=6, nbr_val=20)
@@ -190,7 +190,7 @@ ts.normalize(normalizer="z_score")
 ts_m = ts.Contamination.missing_completely_at_random(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True)
 
 # [OPTIONAL] plot the contaminated time series
-ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap/assets")
+ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets")
 ```
 
 ---
@@ -236,7 +236,7 @@ imputer.score(ts.data, imputer.recov_data)
 ts.print_results(imputer.metrics)
 
 # plot the recovered time series
-ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets")
+ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets")
 ```
 
 ---
@@ -275,7 +275,7 @@ imputer.score(ts.data, imputer.recov_data)
 ts.print_results(imputer.metrics)
 
 # plot the recovered time series
-ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets", display=True)
+ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True)
 
 # save hyperparameters
 utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune")
@@ -343,7 +343,7 @@ ts = TimeSeries()
 print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}")
 
 # load and normalize the timeseries
-ts.load_series(utils.search_path("chlorine"))
+ts.load_series(utils.search_path("forecast-economy"))
 ts.normalize(normalizer="min_max")
 
 # contaminate the time series
@@ -354,7 +354,7 @@ imputer = Imputation.MatrixCompletion.CDRec(ts_m)
 imputer.impute()
 
 # compute print the downstream results
-downstream_config = {"task": "forecast", "model": "prophet"}
+downstream_config = {"task": "forecast", "model": "hw-add"}
 imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
 ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)
 ```
diff --git a/build/lib/imputegap/runner_explainer.py b/build/lib/imputegap/runner_explainer.py
index 07c7709d..c92f2784 100644
--- a/build/lib/imputegap/runner_explainer.py
+++ b/build/lib/imputegap/runner_explainer.py
@@ -9,7 +9,7 @@
 ts_1.load_series(utils.search_path("eeg-alcohol"))
 
 # 3. call the explanation of your dataset with a specific algorithm to gain insight on the Imputation results
-shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, extractor="pycatch22", pattern="mcar", missing_rate=0.25, limit_ratio=1, split_ratio=0.7, file_name="eeg-alcohol", algorithm="cdrec")
+shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, extractor="pycatch22", pattern="mcar", missing_rate=0.25, rate_dataset=1, training_ratio=0.7, file_name="eeg-alcohol", algorithm="cdrec")
 
 # [OPTIONAL] print the results with the impact of each feature.
 Explainer.print(shap_values, shap_details)
\ No newline at end of file
diff --git a/docs/generation/build/doctrees/downstream.doctree b/docs/generation/build/doctrees/downstream.doctree
index 71d6b8bb..d13e9132 100644
Binary files a/docs/generation/build/doctrees/downstream.doctree and b/docs/generation/build/doctrees/downstream.doctree differ
diff --git a/docs/generation/build/doctrees/environment.pickle b/docs/generation/build/doctrees/environment.pickle
index 4a79c7cf..4f08acc2 100644
Binary files a/docs/generation/build/doctrees/environment.pickle and b/docs/generation/build/doctrees/environment.pickle differ
diff --git a/docs/generation/build/doctrees/imputegap.explainer.doctree b/docs/generation/build/doctrees/imputegap.explainer.doctree
index e2b61811..039e85fd 100644
Binary files a/docs/generation/build/doctrees/imputegap.explainer.doctree and b/docs/generation/build/doctrees/imputegap.explainer.doctree differ
diff --git a/docs/generation/build/doctrees/imputegap.imputation.doctree b/docs/generation/build/doctrees/imputegap.imputation.doctree
index f67d5c7f..2b046778 100644
Binary files a/docs/generation/build/doctrees/imputegap.imputation.doctree and b/docs/generation/build/doctrees/imputegap.imputation.doctree differ
diff --git a/docs/generation/build/doctrees/imputegap.manager.doctree b/docs/generation/build/doctrees/imputegap.manager.doctree
index a5811268..3c95cf25 100644
Binary files a/docs/generation/build/doctrees/imputegap.manager.doctree and b/docs/generation/build/doctrees/imputegap.manager.doctree differ
diff --git a/docs/generation/build/doctrees/imputegap.utils.doctree b/docs/generation/build/doctrees/imputegap.utils.doctree
index 11117dac..b83f15ad 100644
Binary files a/docs/generation/build/doctrees/imputegap.utils.doctree and b/docs/generation/build/doctrees/imputegap.utils.doctree differ
diff --git a/docs/generation/build/doctrees/tutorials.doctree b/docs/generation/build/doctrees/tutorials.doctree
index 33f2d749..5324b628 100644
Binary files a/docs/generation/build/doctrees/tutorials.doctree and b/docs/generation/build/doctrees/tutorials.doctree differ
diff --git a/docs/generation/build/html/downstream.html b/docs/generation/build/html/downstream.html
index df9b5bc1..00f5ffee 100644
--- a/docs/generation/build/html/downstream.html
+++ b/docs/generation/build/html/downstream.html
@@ -282,7 +282,7 @@ <h1>Downstream Evaluation<a class="headerlink" href="#downstream-evaluation" tit
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ImputeGAP downstream models for forcasting : </span><span class="si">{</span><span class="n">ts</span><span class="o">.</span><span class="n">downstream_models</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 
 <span class="c1"># load and normalize the timeseries</span>
-<span class="n">ts</span><span class="o">.</span><span class="n">load_series</span><span class="p">(</span><span class="n">utils</span><span class="o">.</span><span class="n">search_path</span><span class="p">(</span><span class="s2">&quot;chlorine&quot;</span><span class="p">))</span>
+<span class="n">ts</span><span class="o">.</span><span class="n">load_series</span><span class="p">(</span><span class="n">utils</span><span class="o">.</span><span class="n">search_path</span><span class="p">(</span><span class="s2">&quot;forecast-economy&quot;</span><span class="p">))</span>
 <span class="n">ts</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">normalizer</span><span class="o">=</span><span class="s2">&quot;min_max&quot;</span><span class="p">)</span>
 
 <span class="c1"># contaminate the time series</span>
@@ -293,7 +293,7 @@ <h1>Downstream Evaluation<a class="headerlink" href="#downstream-evaluation" tit
 <span class="n">imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">()</span>
 
 <span class="c1"># compute print the downstream results</span>
-<span class="n">downstream_config</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;task&quot;</span><span class="p">:</span> <span class="s2">&quot;forecast&quot;</span><span class="p">,</span> <span class="s2">&quot;model&quot;</span><span class="p">:</span> <span class="s2">&quot;prophet&quot;</span><span class="p">}</span>
+<span class="n">downstream_config</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;task&quot;</span><span class="p">:</span> <span class="s2">&quot;forecast&quot;</span><span class="p">,</span> <span class="s2">&quot;model&quot;</span><span class="p">:</span> <span class="s2">&quot;hw-add&quot;</span><span class="p">}</span>
 <span class="n">imputer</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">imputer</span><span class="o">.</span><span class="n">recov_data</span><span class="p">,</span> <span class="n">downstream</span><span class="o">=</span><span class="n">downstream_config</span><span class="p">)</span>
 <span class="n">ts</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">imputer</span><span class="o">.</span><span class="n">downstream_metrics</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="n">imputer</span><span class="o">.</span><span class="n">algorithm</span><span class="p">)</span>
 </pre></div>
diff --git a/docs/generation/build/html/genindex.html b/docs/generation/build/html/genindex.html
index 494ff7b7..757d45e5 100644
--- a/docs/generation/build/html/genindex.html
+++ b/docs/generation/build/html/genindex.html
@@ -454,13 +454,15 @@ <h2>C</h2>
 </li>
         <li><a href="imputegap.evaluation.html#id10">compute_mae() (imputegap.recovery.evaluation.Evaluation method)</a>, <a href="imputegap.evaluation.html#imputegap.recovery.evaluation.Evaluation.compute_mae">[1]</a>
 </li>
-    </ul></td>
-    <td style="width: 33%; vertical-align: top;"><ul>
         <li><a href="imputegap.evaluation.html#id12">compute_mi() (imputegap.recovery.evaluation.Evaluation method)</a>, <a href="imputegap.evaluation.html#imputegap.recovery.evaluation.Evaluation.compute_mi">[1]</a>
 </li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
         <li><a href="imputegap.evaluation.html#id14">compute_rmse() (imputegap.recovery.evaluation.Evaluation method)</a>, <a href="imputegap.evaluation.html#imputegap.recovery.evaluation.Evaluation.compute_rmse">[1]</a>
 </li>
         <li><a href="imputegap.utils.html#imputegap.tools.utils.config_contamination">config_contamination() (in module imputegap.tools.utils)</a>
+</li>
+        <li><a href="imputegap.utils.html#imputegap.tools.utils.config_forecaster">config_forecaster() (in module imputegap.tools.utils)</a>
 </li>
         <li><a href="imputegap.utils.html#imputegap.tools.utils.config_impute_algorithm">config_impute_algorithm() (in module imputegap.tools.utils)</a>
 </li>
@@ -832,6 +834,10 @@ <h2>L</h2>
         <li><a href="imputegap.utils.html#imputegap.tools.utils.list_of_datasets">list_of_datasets() (in module imputegap.tools.utils)</a>
 </li>
         <li><a href="imputegap.utils.html#imputegap.tools.utils.list_of_downstreams">list_of_downstreams() (in module imputegap.tools.utils)</a>
+</li>
+        <li><a href="imputegap.utils.html#imputegap.tools.utils.list_of_downstreams_darts">list_of_downstreams_darts() (in module imputegap.tools.utils)</a>
+</li>
+        <li><a href="imputegap.utils.html#imputegap.tools.utils.list_of_downstreams_sktime">list_of_downstreams_sktime() (in module imputegap.tools.utils)</a>
 </li>
         <li><a href="imputegap.utils.html#imputegap.tools.utils.list_of_optimizers">list_of_optimizers() (in module imputegap.tools.utils)</a>
 </li>
diff --git a/docs/generation/build/html/imputegap.explainer.html b/docs/generation/build/html/imputegap.explainer.html
index 4e5e3a50..326afd4e 100644
--- a/docs/generation/build/html/imputegap.explainer.html
+++ b/docs/generation/build/html/imputegap.explainer.html
@@ -540,7 +540,7 @@ <h4>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="imputegap.recovery.explainer.Explainer.shap_explainer">
-<span class="sig-name descname"><span class="pre">shap_explainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cdrec'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extractor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pycatch'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mcar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ts'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/recovery/explainer.html#Explainer.shap_explainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.recovery.explainer.Explainer.shap_explainer" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">shap_explainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cdrec'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extractor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pycatch'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mcar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ts'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/recovery/explainer.html#Explainer.shap_explainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.recovery.explainer.Explainer.shap_explainer" title="Link to this definition">¶</a></dt>
 <dd><p>Handle parameters and set variables to launch the SHAP model.</p>
 <section id="id13">
 <h4>Parameters<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h4>
@@ -563,9 +563,9 @@ <h4>Parameters<a class="headerlink" href="#id13" title="Link to this heading">¶
 </dd>
 <dt>seed<span class="classifier">bool, optional</span></dt><dd><p>Whether to use a seed for reproducibility (default is True).</p>
 </dd>
-<dt>limit_ratio<span class="classifier">flaot, optional</span></dt><dd><p>Limitation on the number of series for the model (default is 1).</p>
+<dt>rate_dataset<span class="classifier">flaot, optional</span></dt><dd><p>Limitation on the number of series for the model (default is 1).</p>
 </dd>
-<dt>split_ratio<span class="classifier">flaot, optional</span></dt><dd><p>Limitation on the training series for the model (default is 0.6).</p>
+<dt>training_ratio<span class="classifier">flaot, optional</span></dt><dd><p>Limitation on the training series for the model (default is 0.6).</p>
 </dd>
 <dt>file_name<span class="classifier">str, optional</span></dt><dd><p>Name of the dataset file (default is ‘ts’).</p>
 </dd>
diff --git a/docs/generation/build/html/imputegap.html b/docs/generation/build/html/imputegap.html
index e43cd6f6..7b237e13 100644
--- a/docs/generation/build/html/imputegap.html
+++ b/docs/generation/build/html/imputegap.html
@@ -417,11 +417,14 @@ <h2>Subpackages<a class="headerlink" href="#subpackages" title="Link to this hea
 <li class="toctree-l2"><a class="reference internal" href="imputegap.utils.html#submodule-documentation">Submodule Documentation</a></li>
 <li class="toctree-l2"><a class="reference internal" href="imputegap.utils.html#module-imputegap.tools.utils">imputegap.tools.utils module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.config_contamination"><code class="docutils literal notranslate"><span class="pre">config_contamination()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.config_forecaster"><code class="docutils literal notranslate"><span class="pre">config_forecaster()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.config_impute_algorithm"><code class="docutils literal notranslate"><span class="pre">config_impute_algorithm()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.display_title"><code class="docutils literal notranslate"><span class="pre">display_title()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.list_of_algorithms"><code class="docutils literal notranslate"><span class="pre">list_of_algorithms()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.list_of_datasets"><code class="docutils literal notranslate"><span class="pre">list_of_datasets()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.list_of_downstreams"><code class="docutils literal notranslate"><span class="pre">list_of_downstreams()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.list_of_downstreams_darts"><code class="docutils literal notranslate"><span class="pre">list_of_downstreams_darts()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.list_of_downstreams_sktime"><code class="docutils literal notranslate"><span class="pre">list_of_downstreams_sktime()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.list_of_optimizers"><code class="docutils literal notranslate"><span class="pre">list_of_optimizers()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.list_of_patterns"><code class="docutils literal notranslate"><span class="pre">list_of_patterns()</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="imputegap.utils.html#imputegap.tools.utils.load_parameters"><code class="docutils literal notranslate"><span class="pre">load_parameters()</span></code></a></li>
diff --git a/docs/generation/build/html/imputegap.imputation.html b/docs/generation/build/html/imputegap.imputation.html
index c667e433..6ed4dee3 100644
--- a/docs/generation/build/html/imputegap.imputation.html
+++ b/docs/generation/build/html/imputegap.imputation.html
@@ -1824,7 +1824,7 @@ <h6>Returns<a class="headerlink" href="#id98" title="Link to this heading">¶</a
 <section id="id99">
 <h5>Methods<a class="headerlink" href="#id99" title="Link to this heading">¶</a></h5>
 <dl class="simple">
-<dt>impute(self, user_def=True, params=None):</dt><dd><p>Perform imputation using the STMVL algorithm.</p>
+<dt>impute(self, user_def=True, params=None):</dt><dd><p>Perform imputation using the MICE algorithm.</p>
 </dd>
 </dl>
 <dl class="py method">
@@ -1854,7 +1854,7 @@ <h6>Parameters<a class="headerlink" href="#id101" title="Link to this heading">
 <dl>
 <dt>user_def<span class="classifier">bool, optional</span></dt><dd><p>Whether to use user-defined or default parameters (default is True).</p>
 </dd>
-<dt>params<span class="classifier">dict, optional</span></dt><dd><p>Parameters of the STMVL algorithm, if None, default ones are loaded.</p>
+<dt>params<span class="classifier">dict, optional</span></dt><dd><p>Parameters of the MICE algorithm, if None, default ones are loaded.</p>
 <p><strong>Algorithm parameters:</strong></p>
 <dl class="simple">
 <dt>max_iter<span class="classifier">int, optional</span></dt><dd><p>Maximum number of imputation rounds to perform before returning the imputations computed during the final round. (default is 3).</p>
@@ -1871,10 +1871,12 @@ <h6>Parameters<a class="headerlink" href="#id101" title="Link to this heading">
 </section>
 <section id="id102">
 <h6>Returns<a class="headerlink" href="#id102" title="Link to this heading">¶</a></h6>
-<dl class="simple">
+<blockquote>
+<div><dl class="simple">
 <dt>self<span class="classifier">MICE</span></dt><dd><p>The object with <cite>recov_data</cite> set.</p>
 </dd>
 </dl>
+</div></blockquote>
 </section>
 <section id="id103">
 <h6>Example<a class="headerlink" href="#id103" title="Link to this heading">¶</a></h6>
@@ -2098,7 +2100,7 @@ <h6>Returns<a class="headerlink" href="#id118" title="Link to this heading">¶</
 </section>
 <section id="id119">
 <h6>Example<a class="headerlink" href="#id119" title="Link to this heading">¶</a></h6>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">mxgboost_imputer</span> <span class="o">=</span> <span class="n">Imputation</span><span class="o">.</span><span class="n">MachineLearning</span><span class="o">.</span><span class="n">MICE</span><span class="p">(</span><span class="n">incomp_data</span><span class="p">)</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">mxgboost_imputer</span> <span class="o">=</span> <span class="n">Imputation</span><span class="o">.</span><span class="n">MachineLearning</span><span class="o">.</span><span class="n">XGBOOST</span><span class="p">(</span><span class="n">incomp_data</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mxgboost_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">()</span>  <span class="c1"># default parameters for imputation &gt; or</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mxgboost_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">(</span><span class="n">user_def</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;seed&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">})</span>  <span class="c1"># user defined &gt; or</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mxgboost_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">(</span><span class="n">user_def</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;input_data&quot;</span><span class="p">:</span> <span class="n">ts_1</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="s2">&quot;optimizer&quot;</span><span class="p">:</span> <span class="s2">&quot;ray_tune&quot;</span><span class="p">})</span>  <span class="c1"># automl with ray_tune</span>
@@ -3114,10 +3116,10 @@ <h6>Returns<a class="headerlink" href="#id183" title="Link to this heading">¶</
 </section>
 <section id="id184">
 <h6>Example<a class="headerlink" href="#id184" title="Link to this heading">¶</a></h6>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span> <span class="o">=</span> <span class="n">Imputation</span><span class="o">.</span><span class="n">MatrixCompletion</span><span class="o">.</span><span class="n">SVT</span><span class="p">(</span><span class="n">incomp_data</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">()</span>  <span class="c1"># default parameters for imputation &gt; or</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;lags&quot;</span><span class="p">:[],</span> <span class="s2">&quot;K&quot;</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;lambda_f&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;lambda_x&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;lambda_w&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;eta&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;alpha&quot;</span><span class="p">:</span><span class="mf">1000.0</span><span class="p">,</span> <span class="s2">&quot;max_iter&quot;</span><span class="p">:</span><span class="mi">100</span><span class="p">})</span> <span class="c1"># user-defined &gt; or</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">(</span><span class="n">user_def</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;input_data&quot;</span><span class="p">:</span> <span class="n">ts_1</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="s2">&quot;optimizer&quot;</span><span class="p">:</span> <span class="s2">&quot;ray_tune&quot;</span><span class="p">})</span>  <span class="c1"># automl with ray_tune</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span> <span class="o">=</span> <span class="n">Imputation</span><span class="o">.</span><span class="n">MatrixCompletion</span><span class="o">.</span><span class="n">TRMF</span><span class="p">(</span><span class="n">incomp_data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;lags&quot;</span><span class="p">:[],</span> <span class="s2">&quot;K&quot;</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;lambda_f&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;lambda_x&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;lambda_w&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;eta&quot;</span><span class="p">:</span><span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;alpha&quot;</span><span class="p">:</span><span class="mf">1000.0</span><span class="p">,</span> <span class="s2">&quot;max_iter&quot;</span><span class="p">:</span><span class="mi">100</span><span class="p">})</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">trmf_imputer</span><span class="o">.</span><span class="n">impute</span><span class="p">(</span><span class="n">user_def</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;input_data&quot;</span><span class="p">:</span> <span class="n">ts_1</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="s2">&quot;optimizer&quot;</span><span class="p">:</span> <span class="s2">&quot;ray_tune&quot;</span><span class="p">})</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">recov_data</span> <span class="o">=</span> <span class="n">trmf_imputer</span><span class="o">.</span><span class="n">recov_data</span>
 </pre></div>
 </div>
diff --git a/docs/generation/build/html/imputegap.manager.html b/docs/generation/build/html/imputegap.manager.html
index 7f311397..fcf17dd7 100644
--- a/docs/generation/build/html/imputegap.manager.html
+++ b/docs/generation/build/html/imputegap.manager.html
@@ -636,7 +636,7 @@ <h4>Returns<a class="headerlink" href="#id21" title="Link to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="imputegap.recovery.manager.TimeSeries.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">incomp_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recov_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nbr_series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nbr_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(16,</span> <span class="pre">8)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./imputegap/assets'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/recovery/manager.html#TimeSeries.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.recovery.manager.TimeSeries.plot" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">incomp_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recov_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nbr_series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nbr_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(16,</span> <span class="pre">8)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./imputegap_assets'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/recovery/manager.html#TimeSeries.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.recovery.manager.TimeSeries.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot the time series data, including raw, contaminated, or imputed data.</p>
 <section id="id22">
 <h4>Parameters<a class="headerlink" href="#id22" title="Link to this heading">¶</a></h4>
diff --git a/docs/generation/build/html/imputegap.utils.html b/docs/generation/build/html/imputegap.utils.html
index f897a19f..a3d380c7 100644
--- a/docs/generation/build/html/imputegap.utils.html
+++ b/docs/generation/build/html/imputegap.utils.html
@@ -315,12 +315,34 @@ <h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶
 </section>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="imputegap.tools.utils.config_forecaster">
+<span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">config_forecaster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#config_forecaster"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.config_forecaster" title="Link to this definition">¶</a></dt>
+<dd><p>Configure and execute forecaster model for downstream analytics</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model<span class="classifier">str</span></dt><dd><p>name of the forcaster model</p>
+</dd>
+<dt>params<span class="classifier">list of params</span></dt><dd><p>List of paramaters for a forcaster model</p>
+</dd>
+</dl>
+</section>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>Forecaster object (SKTIME/DART)</dt><dd><p>Forecaster object for downstream analytics</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="imputegap.tools.utils.config_impute_algorithm">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">config_impute_algorithm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">incomp_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#config_impute_algorithm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.config_impute_algorithm" title="Link to this definition">¶</a></dt>
 <dd><p>Configure and execute algorithm for selected imputation imputer and pattern.</p>
-<section id="id1">
-<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<section id="id3">
+<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>incomp_data<span class="classifier">TimeSeries</span></dt><dd><p>TimeSeries object containing dataset.</p>
 </dd>
@@ -328,8 +350,8 @@ <h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶<
 </dd>
 </dl>
 </section>
-<section id="id2">
-<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>BaseImputer</dt><dd><p>Configured imputer instance with optimal parameters.</p>
 </dd>
@@ -341,8 +363,8 @@ <h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a>
 <dt class="sig sig-object py" id="imputegap.tools.utils.display_title">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">display_title</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Master</span> <span class="pre">Thesis'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aut</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Quentin</span> <span class="pre">Nater'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ImputeGAP'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">university</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'University</span> <span class="pre">Fribourg'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#display_title"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.display_title" title="Link to this definition">¶</a></dt>
 <dd><p>Display the title and author information.</p>
-<section id="id3">
-<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<section id="id5">
+<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>title<span class="classifier">str, optional</span></dt><dd><p>The title of the thesis (default is “Master Thesis”).</p>
 </dd>
@@ -354,8 +376,8 @@ <h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶<
 </dd>
 </dl>
 </section>
-<section id="id4">
-<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
 <p>None</p>
 </section>
 </dd></dl>
@@ -375,6 +397,16 @@ <h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a>
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">list_of_downstreams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#list_of_downstreams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.list_of_downstreams" title="Link to this definition">¶</a></dt>
 <dd></dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="imputegap.tools.utils.list_of_downstreams_darts">
+<span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">list_of_downstreams_darts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#list_of_downstreams_darts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.list_of_downstreams_darts" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="imputegap.tools.utils.list_of_downstreams_sktime">
+<span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">list_of_downstreams_sktime</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#list_of_downstreams_sktime"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.list_of_downstreams_sktime" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="imputegap.tools.utils.list_of_optimizers">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">list_of_optimizers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#list_of_optimizers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.list_of_optimizers" title="Link to this definition">¶</a></dt>
@@ -389,8 +421,8 @@ <h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a>
 <dt class="sig sig-object py" id="imputegap.tools.utils.load_parameters">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">load_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'cdrec'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'chlorine'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#load_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.load_parameters" title="Link to this definition">¶</a></dt>
 <dd><p>Load default or optimal parameters for algorithms from a TOML file.</p>
-<section id="id5">
-<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<section id="id7">
+<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>query<span class="classifier">str, optional</span></dt><dd><p>‘default’ or ‘optimal’ to load default or optimal parameters (default is “default”).</p>
 </dd>
@@ -404,8 +436,8 @@ <h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶<
 </dd>
 </dl>
 </section>
-<section id="id6">
-<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<section id="id8">
+<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>tuple</dt><dd><p>A tuple containing the loaded parameters for the given algorithm.</p>
 </dd>
@@ -417,8 +449,8 @@ <h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a>
 <dt class="sig sig-object py" id="imputegap.tools.utils.load_share_lib">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">load_share_lib</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lib_cdrec'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#load_share_lib"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.load_share_lib" title="Link to this definition">¶</a></dt>
 <dd><p>Load the shared library based on the operating system.</p>
-<section id="id7">
-<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<section id="id9">
+<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>name<span class="classifier">str, optional</span></dt><dd><p>The name of the shared library (default is “lib_cdrec”).</p>
 </dd>
@@ -426,8 +458,8 @@ <h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶<
 </dd>
 </dl>
 </section>
-<section id="id8">
-<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<section id="id10">
+<h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>ctypes.CDLL</dt><dd><p>The loaded shared library object.</p>
 </dd>
@@ -439,8 +471,8 @@ <h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a>
 <dt class="sig sig-object py" id="imputegap.tools.utils.save_optimization">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">save_optimization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimal_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cdrec'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#save_optimization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.save_optimization" title="Link to this definition">¶</a></dt>
 <dd><p>Save the optimization parameters to a TOML file for later use without recomputing.</p>
-<section id="id9">
-<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<section id="id11">
+<h3>Parameters<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>optimal_params<span class="classifier">dict</span></dt><dd><p>Dictionary of the optimal parameters.</p>
 </dd>
@@ -454,8 +486,8 @@ <h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶<
 </dd>
 </dl>
 </section>
-<section id="id10">
-<h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<section id="id12">
+<h3>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
 <p>None</p>
 </section>
 </dd></dl>
@@ -464,15 +496,15 @@ <h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a
 <dt class="sig sig-object py" id="imputegap.tools.utils.search_path">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">search_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'test'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#search_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.search_path" title="Link to this definition">¶</a></dt>
 <dd><p>Find the accurate path for loading test files.</p>
-<section id="id11">
-<h3>Parameters<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<section id="id13">
+<h3>Parameters<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>set_name<span class="classifier">str, optional</span></dt><dd><p>Name of the dataset (default is “test”).</p>
 </dd>
 </dl>
 </section>
-<section id="id12">
-<h3>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<section id="id14">
+<h3>Returns<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>str</dt><dd><p>The correct file path for the dataset.</p>
 </dd>
@@ -484,8 +516,8 @@ <h3>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a
 <dt class="sig sig-object py" id="imputegap.tools.utils.verification_limitation">
 <span class="sig-prename descclassname"><span class="pre">imputegap.tools.utils.</span></span><span class="sig-name descname"><span class="pre">verification_limitation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">percentage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low_limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high_limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="modules/imputegap/tools/utils.html#verification_limitation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#imputegap.tools.utils.verification_limitation" title="Link to this definition">¶</a></dt>
 <dd><p>Format and verify that the percentage given by the user is within acceptable bounds.</p>
-<section id="id13">
-<h3>Parameters<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<section id="id15">
+<h3>Parameters<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>percentage<span class="classifier">float</span></dt><dd><p>The percentage value to be checked and potentially adjusted.</p>
 </dd>
@@ -495,8 +527,8 @@ <h3>Parameters<a class="headerlink" href="#id13" title="Link to this heading">¶
 </dd>
 </dl>
 </section>
-<section id="id14">
-<h3>Returns<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<section id="id16">
+<h3>Returns<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>float</dt><dd><p>Adjusted percentage based on the limits.</p>
 </dd>
@@ -582,11 +614,14 @@ <h3>Notes<a class="headerlink" href="#notes" title="Link to this heading">¶</a>
 <li><a class="reference internal" href="#submodule-documentation">Submodule Documentation</a></li>
 <li><a class="reference internal" href="#module-imputegap.tools.utils">imputegap.tools.utils module</a><ul>
 <li><a class="reference internal" href="#imputegap.tools.utils.config_contamination"><code class="docutils literal notranslate"><span class="pre">config_contamination()</span></code></a></li>
+<li><a class="reference internal" href="#imputegap.tools.utils.config_forecaster"><code class="docutils literal notranslate"><span class="pre">config_forecaster()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.config_impute_algorithm"><code class="docutils literal notranslate"><span class="pre">config_impute_algorithm()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.display_title"><code class="docutils literal notranslate"><span class="pre">display_title()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.list_of_algorithms"><code class="docutils literal notranslate"><span class="pre">list_of_algorithms()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.list_of_datasets"><code class="docutils literal notranslate"><span class="pre">list_of_datasets()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.list_of_downstreams"><code class="docutils literal notranslate"><span class="pre">list_of_downstreams()</span></code></a></li>
+<li><a class="reference internal" href="#imputegap.tools.utils.list_of_downstreams_darts"><code class="docutils literal notranslate"><span class="pre">list_of_downstreams_darts()</span></code></a></li>
+<li><a class="reference internal" href="#imputegap.tools.utils.list_of_downstreams_sktime"><code class="docutils literal notranslate"><span class="pre">list_of_downstreams_sktime()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.list_of_optimizers"><code class="docutils literal notranslate"><span class="pre">list_of_optimizers()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.list_of_patterns"><code class="docutils literal notranslate"><span class="pre">list_of_patterns()</span></code></a></li>
 <li><a class="reference internal" href="#imputegap.tools.utils.load_parameters"><code class="docutils literal notranslate"><span class="pre">load_parameters()</span></code></a></li>
diff --git a/docs/generation/build/html/modules/imputegap/recovery/benchmark.html b/docs/generation/build/html/modules/imputegap/recovery/benchmark.html
index 9ca4fce8..749ad202 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/benchmark.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/benchmark.html
@@ -390,10 +390,13 @@ <h1>Source code for imputegap.recovery.benchmark</h1><div class="highlight"><pre
 
                                     <span class="c1"># Add scores and times</span>
                                     <span class="k">for</span> <span class="n">score_key</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">level_value</span><span class="p">[</span><span class="s2">&quot;scores&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                                        <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span> <span class="p">:</span>
+                                            <span class="n">v</span> <span class="o">=</span> <span class="mi">0</span>
                                         <span class="n">merger</span><span class="p">[</span><span class="s2">&quot;scores&quot;</span><span class="p">][</span><span class="n">score_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">merger</span><span class="p">[</span><span class="s2">&quot;scores&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">score_key</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">v</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span>
                                     <span class="k">for</span> <span class="n">time_key</span><span class="p">,</span> <span class="n">time_value</span> <span class="ow">in</span> <span class="n">level_value</span><span class="p">[</span><span class="s2">&quot;times&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                                        <span class="n">merger</span><span class="p">[</span><span class="s2">&quot;times&quot;</span><span class="p">][</span><span class="n">time_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                                                    <span class="n">merger</span><span class="p">[</span><span class="s2">&quot;times&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">time_key</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">time_value</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span>
+                                        <span class="k">if</span> <span class="n">time_value</span> <span class="ow">is</span> <span class="kc">None</span> <span class="p">:</span>
+                                            <span class="n">time_value</span> <span class="o">=</span> <span class="mi">0</span>
+                                        <span class="n">merger</span><span class="p">[</span><span class="s2">&quot;times&quot;</span><span class="p">][</span><span class="n">time_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">merger</span><span class="p">[</span><span class="s2">&quot;times&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">time_key</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">time_value</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span>
 
             <span class="n">results_avg</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">merged_dict</span><span class="p">)</span>
 
diff --git a/docs/generation/build/html/modules/imputegap/recovery/explainer.html b/docs/generation/build/html/modules/imputegap/recovery/explainer.html
index ea1f17e5..c048180f 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/explainer.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/explainer.html
@@ -696,7 +696,7 @@ <h1>Source code for imputegap.recovery.explainer</h1><div class="highlight"><pre
         <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">config</span> <span class="o">=</span> <span class="n">Explainer</span><span class="o">.</span><span class="n">load_configuration</span><span class="p">()</span>
         <span class="n">plots_categories</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="n">extractor</span><span class="p">][</span><span class="s1">&#39;categories&#39;</span><span class="p">]</span>
 
-        <span class="n">path_file</span> <span class="o">=</span> <span class="s2">&quot;./assets/shap/&quot;</span>
+        <span class="n">path_file</span> <span class="o">=</span> <span class="s2">&quot;./imputegap_assets/shap/&quot;</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path_file</span><span class="p">):</span>
             <span class="n">path_file</span> <span class="o">=</span> <span class="s2">&quot;./imputegap&quot;</span> <span class="o">+</span> <span class="n">path_file</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
 
@@ -925,7 +925,7 @@ <h1>Source code for imputegap.recovery.explainer</h1><div class="highlight"><pre
 <div class="viewcode-block" id="Explainer.shap_explainer">
 <a class="viewcode-back" href="../../../imputegap.explainer.html#imputegap.recovery.explainer.Explainer.shap_explainer">[docs]</a>
     <span class="k">def</span><span class="w"> </span><span class="nf">shap_explainer</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="s2">&quot;cdrec&quot;</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">extractor</span><span class="o">=</span><span class="s2">&quot;pycatch&quot;</span><span class="p">,</span> <span class="n">pattern</span><span class="o">=</span><span class="s2">&quot;mcar&quot;</span><span class="p">,</span> <span class="n">missing_rate</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-                       <span class="n">block_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">limit_ratio</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">split_ratio</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
+                       <span class="n">block_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">rate_dataset</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">training_ratio</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
                        <span class="n">file_name</span><span class="o">=</span><span class="s2">&quot;ts&quot;</span><span class="p">,</span> <span class="n">display</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Handle parameters and set variables to launch the SHAP model.</span>
@@ -950,9 +950,9 @@ <h1>Source code for imputegap.recovery.explainer</h1><div class="highlight"><pre
 <span class="sd">            Size of the uncontaminated section at the beginning of the time series (default is 0.1).</span>
 <span class="sd">        seed : bool, optional</span>
 <span class="sd">            Whether to use a seed for reproducibility (default is True).</span>
-<span class="sd">        limit_ratio : flaot, optional</span>
+<span class="sd">        rate_dataset : flaot, optional</span>
 <span class="sd">            Limitation on the number of series for the model (default is 1).</span>
-<span class="sd">        split_ratio : flaot, optional</span>
+<span class="sd">        training_ratio : flaot, optional</span>
 <span class="sd">            Limitation on the training series for the model (default is 0.6).</span>
 <span class="sd">        file_name : str, optional</span>
 <span class="sd">            Name of the dataset file (default is &#39;ts&#39;).</span>
@@ -978,18 +978,18 @@ <h1>Source code for imputegap.recovery.explainer</h1><div class="highlight"><pre
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>  <span class="c1"># Record start time</span>
 
-        <span class="k">if</span> <span class="n">limit_ratio</span> <span class="o">&lt;</span> <span class="mf">0.05</span> <span class="ow">or</span> <span class="n">limit_ratio</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">rate_dataset</span> <span class="o">&lt;</span> <span class="mf">0.05</span> <span class="ow">or</span> <span class="n">rate_dataset</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">limit percentage higher than 100%, reduce to 100</span><span class="si">% o</span><span class="s2">f the dataset&quot;</span><span class="p">)</span>
-            <span class="n">limit_ratio</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="n">rate_dataset</span> <span class="o">=</span> <span class="mi">1</span>
 
         <span class="n">M</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">limit</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">M</span> <span class="o">*</span> <span class="n">limit_ratio</span><span class="p">)</span>
+        <span class="n">limit</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">M</span> <span class="o">*</span> <span class="n">rate_dataset</span><span class="p">)</span>
 
-        <span class="k">if</span> <span class="n">split_ratio</span> <span class="o">&lt;</span> <span class="mf">0.05</span> <span class="ow">or</span> <span class="n">split_ratio</span> <span class="o">&gt;</span> <span class="mf">0.95</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">training_ratio</span> <span class="o">&lt;</span> <span class="mf">0.05</span> <span class="ow">or</span> <span class="n">training_ratio</span> <span class="o">&gt;</span> <span class="mf">0.95</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">split ratio to small or to high, reduce to 60</span><span class="si">% o</span><span class="s2">f the dataset&quot;</span><span class="p">)</span>
-            <span class="n">split_ratio</span> <span class="o">=</span> <span class="mf">0.6</span>
+            <span class="n">training_ratio</span> <span class="o">=</span> <span class="mf">0.6</span>
 
-        <span class="n">training_ratio</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">limit</span> <span class="o">*</span> <span class="n">split_ratio</span><span class="p">)</span>
+        <span class="n">training_ratio</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">limit</span> <span class="o">*</span> <span class="n">training_ratio</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">limit</span> <span class="o">&gt;</span> <span class="n">M</span><span class="p">:</span>
             <span class="n">limit</span> <span class="o">=</span> <span class="n">M</span>
@@ -1008,7 +1008,7 @@ <h1>Source code for imputegap.recovery.explainer</h1><div class="highlight"><pre
         <span class="n">input_data_matrices</span><span class="p">,</span> <span class="n">obfuscated_matrices</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
         <span class="n">output_metrics</span><span class="p">,</span> <span class="n">output_rmse</span><span class="p">,</span> <span class="n">input_params</span><span class="p">,</span> <span class="n">input_params_full</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
 
-        <span class="k">if</span> <span class="n">extractor</span> <span class="o">==</span> <span class="s2">&quot;pycatch&quot;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">extractor</span> <span class="o">==</span> <span class="s2">&quot;pycatch&quot;</span> <span class="ow">or</span> <span class="n">extractor</span> <span class="o">==</span> <span class="s2">&quot;pycatch22&quot;</span><span class="p">:</span>
             <span class="n">categories</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">Explainer</span><span class="o">.</span><span class="n">load_configuration</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">current_series</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">limit</span><span class="p">):</span>
@@ -1023,7 +1023,7 @@ <h1>Source code for imputegap.recovery.explainer</h1><div class="highlight"><pre
             <span class="n">input_data_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
             <span class="n">obfuscated_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">incomp_data</span><span class="p">)</span>
 
-            <span class="k">if</span> <span class="n">extractor</span> <span class="o">==</span> <span class="s2">&quot;pycatch&quot;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">extractor</span> <span class="o">==</span> <span class="s2">&quot;pycatch&quot;</span> <span class="ow">or</span> <span class="n">extractor</span> <span class="o">==</span> <span class="s2">&quot;pycatch22&quot;</span><span class="p">:</span>
                 <span class="n">catch_fct</span><span class="p">,</span> <span class="n">descriptions</span> <span class="o">=</span> <span class="n">Explainer</span><span class="o">.</span><span class="n">extractor_pycatch</span><span class="p">(</span><span class="n">incomp_data</span><span class="p">,</span> <span class="n">categories</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
                 <span class="n">extracted_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">catch_fct</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
             <span class="k">elif</span> <span class="n">extractor</span> <span class="o">==</span> <span class="s2">&quot;tsfel&quot;</span><span class="p">:</span>
diff --git a/docs/generation/build/html/modules/imputegap/recovery/imputation.html b/docs/generation/build/html/modules/imputegap/recovery/imputation.html
index 3c945555..b6834755 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/imputation.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/imputation.html
@@ -835,11 +835,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; interpolation_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; interpolation_imputer.impute(user_def=True, params={&quot;method&quot;:&quot;linear&quot;, &quot;poly_order&quot;:2})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; interpolation_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = interpolation_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; interpolation_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; interpolation_imputer.impute(user_def=True, params={&quot;method&quot;:&quot;linear&quot;, &quot;poly_order&quot;:2})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; interpolation_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = interpolation_imputer.recov_data</span>
 <span class="sd">                &quot;&quot;&quot;</span>
                 <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">method</span><span class="p">,</span> <span class="n">poly_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_params</span><span class="p">(</span><span class="n">user_def</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
@@ -892,11 +892,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; knn_imputer = Imputation.Statistics.KNN(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; knn_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; knn_imputer.impute(user_def=True, params={&#39;k&#39;: 5, &#39;weights&#39;: &quot;uniform&quot;})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; knn_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = knn_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; knn_imputer = Imputation.Statistics.KNN(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; knn_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; knn_imputer.impute(user_def=True, params={&#39;k&#39;: 5, &#39;weights&#39;: &quot;uniform&quot;})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; knn_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = knn_imputer.recov_data</span>
 <span class="sd">                &quot;&quot;&quot;</span>
                 <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">k</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_params</span><span class="p">(</span><span class="n">user_def</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
@@ -1043,11 +1043,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; cdrec_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; cdrec_imputer.impute(user_def=True, params={&#39;rank&#39;: 5, &#39;epsilon&#39;: 0.01, &#39;iterations&#39;: 100})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; cdrec_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = cdrec_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; cdrec_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; cdrec_imputer.impute(user_def=True, params={&#39;rank&#39;: 5, &#39;epsilon&#39;: 0.01, &#39;iterations&#39;: 100})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; cdrec_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = cdrec_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1107,11 +1107,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; i_svd_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; i_svd_imputer.impute(params={&#39;rank&#39;: 5}) # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; i_svd_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = i_svd_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; i_svd_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; i_svd_imputer.impute(params={&#39;rank&#39;: 5}) # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; i_svd_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = i_svd_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1170,11 +1170,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; grouse_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; grouse_imputer.impute(params={&#39;max_rank&#39;: 5}) # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; grouse_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = grouse_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; grouse_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; grouse_imputer.impute(params={&#39;max_rank&#39;: 5}) # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; grouse_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = grouse_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1236,11 +1236,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; rosl_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; rosl_imputer.impute(params={&#39;rank&#39;: 5, &#39;regularization&#39;: 10}) # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; rosl_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = rosl_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; rosl_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; rosl_imputer.impute(params={&#39;rank&#39;: 5, &#39;regularization&#39;: 10}) # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; rosl_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = rosl_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1298,11 +1298,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; soft_impute_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; soft_impute_imputer.impute(params={&#39;max_rank&#39;: 5}) # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; soft_impute_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = soft_impute_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; soft_impute_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; soft_impute_imputer.impute(params={&#39;max_rank&#39;: 5}) # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; soft_impute_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = soft_impute_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1367,11 +1367,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; spirit_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; spirit_imputer.impute(params={&#39;k&#39;: 2, &#39;w&#39;: 5, &#39;lambda_value&#39;: 0.85}) # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; spirit_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = spirit_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; spirit_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; spirit_imputer.impute(params={&#39;k&#39;: 2, &#39;w&#39;: 5, &#39;lambda_value&#39;: 0.85}) # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; spirit_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = spirit_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1430,11 +1430,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; svt_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; svt_imputer.impute(params={&#39;tau&#39;: 1}) # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; svt_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = svt_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; svt_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; svt_imputer.impute(params={&#39;tau&#39;: 1}) # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; svt_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = svt_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1509,18 +1509,18 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; trmf_imputer = Imputation.MatrixCompletion.SVT(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; trmf_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; trmf_imputer.impute(params={&quot;lags&quot;:[], &quot;K&quot;:-1, &quot;lambda_f&quot;:1.0, &quot;lambda_x&quot;:1.0, &quot;lambda_w&quot;:1.0, &quot;eta&quot;:1.0, &quot;alpha&quot;:1000.0, &quot;max_iter&quot;:100}) # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; trmf_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = trmf_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; trmf_imputer = Imputation.MatrixCompletion.TRMF(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; trmf_imputer.impute()</span>
+<span class="sd">                    &gt;&gt;&gt; trmf_imputer.impute(params={&quot;lags&quot;:[], &quot;K&quot;:-1, &quot;lambda_f&quot;:1.0, &quot;lambda_x&quot;:1.0, &quot;lambda_w&quot;:1.0, &quot;eta&quot;:1.0, &quot;alpha&quot;:1000.0, &quot;max_iter&quot;:100})</span>
+<span class="sd">                    &gt;&gt;&gt; trmf_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = trmf_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
 <span class="sd">                H.-F. Yu, N. Rao, and I. S. Dhillon, &quot;Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction,&quot; in *Advances in Neural Information Processing Systems*, vol. 29, 2016. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2016/file/85422afb467e9456013a2a51d4dff702-Paper.pdf</span>
 <span class="sd">                &quot;&quot;&quot;</span>
                 <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">lags</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">lambda_f</span><span class="p">,</span> <span class="n">lambda_x</span><span class="p">,</span> <span class="n">lambda_w</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">max_iter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_params</span><span class="p">(</span><span class="n">user_def</span><span class="p">,</span> <span class="n">params</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">lags</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">lambda_f</span><span class="p">,</span> <span class="n">lambda_x</span><span class="p">,</span> <span class="n">lambda_w</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">max_iter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_params</span><span class="p">(</span><span class="n">user_def</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
                 <span class="k">else</span><span class="p">:</span>
                     <span class="n">lags</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">lambda_f</span><span class="p">,</span> <span class="n">lambda_x</span><span class="p">,</span> <span class="n">lambda_w</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">max_iter</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="s2">&quot;default&quot;</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">algorithm</span><span class="p">)</span>
 
@@ -1604,11 +1604,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; mf_imputer = Imputation.MachineLearning.MissForest(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; mf_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mf_imputer.impute(user_def=True, params={&quot;n_estimators&quot;:10, &quot;max_iter&quot;:3, &quot;max_features&quot;:&quot;sqrt&quot;, &quot;seed&quot;: 42})  # user defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mf_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = mf_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; mf_imputer = Imputation.MachineLearning.MissForest(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; mf_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mf_imputer.impute(user_def=True, params={&quot;n_estimators&quot;:10, &quot;max_iter&quot;:3, &quot;max_features&quot;:&quot;sqrt&quot;, &quot;seed&quot;: 42})  # user defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mf_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = mf_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1637,7 +1637,7 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 <span class="sd">            Methods</span>
 <span class="sd">            -------</span>
 <span class="sd">            impute(self, user_def=True, params=None):</span>
-<span class="sd">                Perform imputation using the STMVL algorithm.</span>
+<span class="sd">                Perform imputation using the MICE algorithm.</span>
 <span class="sd">            &quot;&quot;&quot;</span>
             <span class="n">algorithm</span> <span class="o">=</span> <span class="s2">&quot;mice&quot;</span>
 
@@ -1650,33 +1650,33 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 <span class="sd">                Parameters</span>
 <span class="sd">                ----------</span>
 <span class="sd">                user_def : bool, optional</span>
-<span class="sd">                    Whether to use user-defined or default parameters (default is True).</span>
+<span class="sd">                    Whether to use user-defined or default parameters (default is True). \n</span>
 <span class="sd">                params : dict, optional</span>
-<span class="sd">                    Parameters of the STMVL algorithm, if None, default ones are loaded.</span>
+<span class="sd">                    Parameters of the MICE algorithm, if None, default ones are loaded. \n</span>
 
 <span class="sd">                    **Algorithm parameters:**</span>
 
 <span class="sd">                    max_iter : int, optional</span>
-<span class="sd">                        Maximum number of imputation rounds to perform before returning the imputations computed during the final round. (default is 3).</span>
+<span class="sd">                        Maximum number of imputation rounds to perform before returning the imputations computed during the final round. (default is 3). \n</span>
 <span class="sd">                    tol : float, optional</span>
-<span class="sd">                        Tolerance of the stopping condition. (default is 0.001).</span>
+<span class="sd">                        Tolerance of the stopping condition. (default is 0.001). \n</span>
 <span class="sd">                    initial_strategy : str, optional</span>
-<span class="sd">                        Which strategy to use to initialize the missing values. {‘mean’, ‘median’, ‘most_frequent’, ‘constant’} (default is &quot;means&quot;).</span>
+<span class="sd">                        Which strategy to use to initialize the missing values. {‘mean’, ‘median’, ‘most_frequent’, ‘constant’} (default is &quot;means&quot;). \n</span>
 <span class="sd">                    seed : int, optional</span>
-<span class="sd">                        The seed of the pseudo random number generator to use. Randomizes selection of estimator features (default is 42).</span>
+<span class="sd">                        The seed of the pseudo random number generator to use. Randomizes selection of estimator features (default is 42). \n</span>
 
 <span class="sd">                Returns</span>
 <span class="sd">                -------</span>
-<span class="sd">                self : MICE</span>
-<span class="sd">                    The object with `recov_data` set.</span>
+<span class="sd">                    self : MICE</span>
+<span class="sd">                        The object with `recov_data` set.</span>
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; mice_imputer = Imputation.MachineLearning.MICE(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; mice_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mice_imputer.impute(user_def=True, params={&quot;max_iter&quot;:3, &quot;tol&quot;:0.001, &quot;initial_strategy&quot;:&quot;mean&quot;, &quot;seed&quot;: 42})  # user defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mice_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = mice_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; mice_imputer = Imputation.MachineLearning.MICE(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; mice_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mice_imputer.impute(user_def=True, params={&quot;max_iter&quot;:3, &quot;tol&quot;:0.001, &quot;initial_strategy&quot;:&quot;mean&quot;, &quot;seed&quot;: 42})  # user defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mice_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = mice_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1736,11 +1736,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; mxgboost_imputer = Imputation.MachineLearning.MICE(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; mxgboost_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mxgboost_imputer.impute(user_def=True, params={&quot;n_estimators&quot;:3, &quot;seed&quot;: 42})  # user defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mxgboost_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = mxgboost_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; mxgboost_imputer = Imputation.MachineLearning.XGBOOST(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; mxgboost_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mxgboost_imputer.impute(user_def=True, params={&quot;n_estimators&quot;:3, &quot;seed&quot;: 42})  # user defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mxgboost_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = mxgboost_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1797,11 +1797,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; iim_imputer = Imputation.MachineLearning.IIM(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; iim_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; iim_imputer.impute(user_def=True, params={&#39;learning_neighbors&#39;: 10})  # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; iim_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = iim_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; iim_imputer = Imputation.MachineLearning.IIM(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; iim_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; iim_imputer.impute(user_def=True, params={&#39;learning_neighbors&#39;: 10})  # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; iim_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = iim_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1883,11 +1883,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; stmvl_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; stmvl_imputer.impute(user_def=True, params={&#39;window_size&#39;: 7, &#39;learning_rate&#39;:0.01, &#39;gamma&#39;:0.85, &#39;alpha&#39;: 7})  # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; stmvl_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = stmvl_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; stmvl_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; stmvl_imputer.impute(user_def=True, params={&#39;window_size&#39;: 7, &#39;learning_rate&#39;:0.01, &#39;gamma&#39;:0.85, &#39;alpha&#39;: 7})  # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; stmvl_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = stmvl_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -1949,11 +1949,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; dynammo_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; dynammo_imputer.impute(params={&#39;h&#39;: 5, &#39;max_iteration&#39;: 100, &#39;approximation&#39;: True}) # user-defined  &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; dynammo_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = dynammo_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; dynammo_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; dynammo_imputer.impute(params={&#39;h&#39;: 5, &#39;max_iteration&#39;: 100, &#39;approximation&#39;: True}) # user-defined  &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; dynammo_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = dynammo_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2010,11 +2010,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; tkcm_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; tkcm_imputer.impute(params={&#39;rank&#39;: 5})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; tkcm_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = tkcm_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; tkcm_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; tkcm_imputer.impute(params={&#39;rank&#39;: 5})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; tkcm_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = tkcm_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2106,11 +2106,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; mrnn_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mrnn_imputer.impute(user_def=True, params={&#39;hidden_dim&#39;: 10, &#39;learning_rate&#39;:0.01, &#39;iterations&#39;:50, &#39;sequence_length&#39;: 7})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mrnn_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = mrnn_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; mrnn_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mrnn_imputer.impute(user_def=True, params={&#39;hidden_dim&#39;: 10, &#39;learning_rate&#39;:0.01, &#39;iterations&#39;:50, &#39;sequence_length&#39;: 7})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mrnn_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;bayesian&quot;, &quot;options&quot;: {&quot;n_calls&quot;: 2}})  # automl with bayesian</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = mrnn_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2175,11 +2175,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; brits_imputer = Imputation.DeepLearning.BRITS(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; brits_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; brits_imputer.impute(params={&quot;model&quot;: &quot;brits&quot;, &quot;epoch&quot;: 2, &quot;batch_size&quot;: 10, &quot;nbr_features&quot;: 1, &quot;hidden_layer&quot;: 64})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; brits_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = brits_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; brits_imputer = Imputation.DeepLearning.BRITS(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; brits_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; brits_imputer.impute(params={&quot;model&quot;: &quot;brits&quot;, &quot;epoch&quot;: 2, &quot;batch_size&quot;: 10, &quot;nbr_features&quot;: 1, &quot;hidden_layer&quot;: 64})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; brits_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = brits_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2239,11 +2239,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; deep_mvi_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; deep_mvi_imputer.impute(params={&quot;max_epoch&quot;: 10, &quot;patience&quot;: 2})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; deep_mvi_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = deep_mvi_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; deep_mvi_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; deep_mvi_imputer.impute(params={&quot;max_epoch&quot;: 10, &quot;patience&quot;: 2})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; deep_mvi_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = deep_mvi_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2315,11 +2315,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; mpin_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mpin_imputer.impute(params={&quot;incre_mode&quot;: &quot;data+state&quot;, &quot;window&quot;: 1, &quot;k&quot;: 15, &quot;learning_rate&quot;: 0.001, &quot;weight_decay&quot;: 0.2, &quot;epochs&quot;: 6, &quot;num_of_iteration&quot;: 6, &quot;threshold&quot;: 0.50, &quot;base&quot;: &quot;GCN&quot;})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; mpin_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = mpin_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; mpin_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mpin_imputer.impute(params={&quot;incre_mode&quot;: &quot;data+state&quot;, &quot;window&quot;: 1, &quot;k&quot;: 15, &quot;learning_rate&quot;: 0.001, &quot;weight_decay&quot;: 0.2, &quot;epochs&quot;: 6, &quot;num_of_iteration&quot;: 6, &quot;threshold&quot;: 0.50, &quot;base&quot;: &quot;GCN&quot;})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; mpin_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = mpin_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2382,11 +2382,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; pristi_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; pristi_imputer.impute(params={&quot;target_strategy&quot;:&quot;hybrid&quot;, &quot;unconditional&quot;:True, &quot;seed&quot;:42, &quot;device&quot;:&quot;cpu&quot;})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; pristi_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = pristi_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; pristi_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; pristi_imputer.impute(params={&quot;target_strategy&quot;:&quot;hybrid&quot;, &quot;unconditional&quot;:True, &quot;seed&quot;:42, &quot;device&quot;:&quot;cpu&quot;})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; pristi_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # automl with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = pristi_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2455,11 +2455,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; miss_net_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; miss_net_imputer.impute(user_def=True, params={&#39;alpha&#39;: 0.5, &#39;beta&#39;:0.1, &#39;L&#39;:10, &#39;n_cl&#39;: 1, &#39;max_iteration&#39;:20, &#39;tol&#39;:5, &#39;random_init&#39;:False})  # user-defined &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; miss_net_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = miss_net_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; miss_net_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; miss_net_imputer.impute(user_def=True, params={&#39;alpha&#39;: 0.5, &#39;beta&#39;:0.1, &#39;L&#39;:10, &#39;n_cl&#39;: 1, &#39;max_iteration&#39;:20, &#39;tol&#39;:5, &#39;random_init&#39;:False})  # user-defined &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; miss_net_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = miss_net_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2528,11 +2528,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; gain_imputer = Imputation.DeepLearning.GAIN(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; gain_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; gain_imputer.impute(user_def=True, params={&quot;batch_size&quot;:32, &quot;hint_rate&quot;:0.9, &quot;alpha&quot;:10, &quot;epoch&quot;:100})  # user defined&gt; or</span>
-<span class="sd">                &gt;&gt;&gt; gain_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = gain_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; gain_imputer = Imputation.DeepLearning.GAIN(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; gain_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; gain_imputer.impute(user_def=True, params={&quot;batch_size&quot;:32, &quot;hint_rate&quot;:0.9, &quot;alpha&quot;:10, &quot;epoch&quot;:100})  # user defined&gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; gain_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = gain_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2613,11 +2613,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; grin_imputer = Imputation.DeepLearning.GRIN(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; grin_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; grin_imputer.impute(user_def=True, params={&quot;d_hidden&quot;:32, &quot;lr&quot;:0.001, &quot;batch_size&quot;:32, &quot;window&quot;:1, &quot;alpha&quot;:10.0, &quot;patience&quot;:4, &quot;epochs&quot;:20, &quot;workers&quot;:2})  # user defined&gt; or</span>
-<span class="sd">                &gt;&gt;&gt; grin_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = grin_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; grin_imputer = Imputation.DeepLearning.GRIN(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; grin_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; grin_imputer.impute(user_def=True, params={&quot;d_hidden&quot;:32, &quot;lr&quot;:0.001, &quot;batch_size&quot;:32, &quot;window&quot;:1, &quot;alpha&quot;:10.0, &quot;patience&quot;:4, &quot;epochs&quot;:20, &quot;workers&quot;:2})  # user defined&gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; grin_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = grin_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2704,11 +2704,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; bay_otide_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; bay_otide_imputer.impute(user_def=True, params={&quot;K_trend&quot;:20, &quot;K_season&quot;:2, &quot;n_season&quot;:5, &quot;K_bias&quot;:1, &quot;time_scale&quot;:1, &quot;a0&quot;:0.6, &quot;b0&quot;:2.5, &quot;v&quot;:0.5})  # user defined&gt; or</span>
-<span class="sd">                &gt;&gt;&gt; bay_otide_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = bay_otide_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; bay_otide_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; bay_otide_imputer.impute(user_def=True, params={&quot;K_trend&quot;:20, &quot;K_season&quot;:2, &quot;n_season&quot;:5, &quot;K_bias&quot;:1, &quot;time_scale&quot;:1, &quot;a0&quot;:0.6, &quot;b0&quot;:2.5, &quot;v&quot;:0.5})  # user defined&gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; bay_otide_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = bay_otide_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2776,11 +2776,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; hkmf_t_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; hkmf_t_imputer.impute(user_def=True, params={&quot;tags&quot;:None, &quot;data_names&quot;:None, &quot;epoch&quot;:5})  # user defined&gt; or</span>
-<span class="sd">                &gt;&gt;&gt; hkmf_t_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = hkmf_t_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; hkmf_t_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; hkmf_t_imputer.impute(user_def=True, params={&quot;tags&quot;:None, &quot;data_names&quot;:None, &quot;epoch&quot;:5})  # user defined&gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; hkmf_t_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = hkmf_t_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
@@ -2869,11 +2869,11 @@ <h1>Source code for imputegap.recovery.imputation</h1><div class="highlight"><pr
 
 <span class="sd">                Example</span>
 <span class="sd">                -------</span>
-<span class="sd">                &gt;&gt;&gt; bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data)</span>
-<span class="sd">                &gt;&gt;&gt; bit_graph_imputer.impute()  # default parameters for imputation &gt; or</span>
-<span class="sd">                &gt;&gt;&gt; bit_graph_imputer.impute(user_def=True, params={&quot;node_number&quot;:-1, &quot;kernel_set&quot;:[1], &quot;dropout&quot;:0.1, &quot;subgraph_size&quot;:5, &quot;node_dim&quot;:3, &quot;seq_len&quot;:1, &quot;lr&quot;:0.001, &quot;epoch&quot;:10, &quot;seed&quot;:42})  # user defined&gt; or</span>
-<span class="sd">                &gt;&gt;&gt; bit_graph_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
-<span class="sd">                &gt;&gt;&gt; recov_data = bit_graph_imputer.recov_data</span>
+<span class="sd">                    &gt;&gt;&gt; bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data)</span>
+<span class="sd">                    &gt;&gt;&gt; bit_graph_imputer.impute()  # default parameters for imputation &gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; bit_graph_imputer.impute(user_def=True, params={&quot;node_number&quot;:-1, &quot;kernel_set&quot;:[1], &quot;dropout&quot;:0.1, &quot;subgraph_size&quot;:5, &quot;node_dim&quot;:3, &quot;seq_len&quot;:1, &quot;lr&quot;:0.001, &quot;epoch&quot;:10, &quot;seed&quot;:42})  # user defined&gt; or</span>
+<span class="sd">                    &gt;&gt;&gt; bit_graph_imputer.impute(user_def=False, params={&quot;input_data&quot;: ts_1.data, &quot;optimizer&quot;: &quot;ray_tune&quot;})  # auto-ml with ray_tune</span>
+<span class="sd">                    &gt;&gt;&gt; recov_data = bit_graph_imputer.recov_data</span>
 
 <span class="sd">                References</span>
 <span class="sd">                ----------</span>
diff --git a/docs/generation/build/html/modules/imputegap/recovery/manager.html b/docs/generation/build/html/modules/imputegap/recovery/manager.html
index 8cd958c5..a8d3df1a 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/manager.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/manager.html
@@ -598,7 +598,7 @@ <h1>Source code for imputegap.recovery.manager</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="TimeSeries.plot">
 <a class="viewcode-back" href="../../../imputegap.manager.html#imputegap.recovery.manager.TimeSeries.plot">[docs]</a>
     <span class="k">def</span><span class="w"> </span><span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_data</span><span class="p">,</span> <span class="n">incomp_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">recov_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbr_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">series_range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-             <span class="n">subplot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap/assets&quot;</span><span class="p">,</span> <span class="n">display</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+             <span class="n">subplot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap_assets&quot;</span><span class="p">,</span> <span class="n">display</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot the time series data, including raw, contaminated, or imputed data.</span>
 
@@ -719,7 +719,7 @@ <h1>Source code for imputegap.recovery.manager</h1><div class="highlight"><pre>
                     <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Series &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
                     <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Timestamp&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
                     <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Values&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-                    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fancybox</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">framealpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
                     <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
 
                 <span class="n">number_of_series</span> <span class="o">+=</span> <span class="mi">1</span>
diff --git a/docs/generation/build/html/modules/imputegap/tools/utils.html b/docs/generation/build/html/modules/imputegap/tools/utils.html
index 3bae386a..f8d1f339 100644
--- a/docs/generation/build/html/modules/imputegap/tools/utils.html
+++ b/docs/generation/build/html/modules/imputegap/tools/utils.html
@@ -409,6 +409,85 @@ <h1>Source code for imputegap.tools.utils</h1><div class="highlight"><pre>
 
 
 
+<div class="viewcode-block" id="config_forecaster">
+<a class="viewcode-back" href="../../../imputegap.utils.html#imputegap.tools.utils.config_forecaster">[docs]</a>
+<span class="k">def</span><span class="w"> </span><span class="nf">config_forecaster</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Configure and execute forecaster model for downstream analytics</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        model : str</span>
+<span class="sd">            name of the forcaster model</span>
+<span class="sd">        params : list of params</span>
+<span class="sd">            List of paramaters for a forcaster model</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Forecaster object (SKTIME/DART)</span>
+<span class="sd">            Forecaster object for downstream analytics</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;prophet&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.fbprophet</span><span class="w"> </span><span class="kn">import</span> <span class="n">Prophet</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">Prophet</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;exp-smoothing&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.exp_smoothing</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExponentialSmoothing</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">ExponentialSmoothing</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;nbeats&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">darts.models</span><span class="w"> </span><span class="kn">import</span> <span class="n">NBEATSModel</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">NBEATSModel</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;xgboost&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">darts.models.forecasting.xgboost</span><span class="w"> </span><span class="kn">import</span> <span class="n">XGBModel</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">XGBModel</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;lightgbm&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">darts.models.forecasting.lgbm</span><span class="w"> </span><span class="kn">import</span> <span class="n">LightGBMModel</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">LightGBMModel</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;lstm&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">darts.models.forecasting.rnn_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">RNNModel</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">RNNModel</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;deepar&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">darts.models.forecasting.rnn_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">RNNModel</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">RNNModel</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;transformer&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">darts.models.forecasting.transformer_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">TransformerModel</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">TransformerModel</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;hw-add&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.exp_smoothing</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExponentialSmoothing</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">ExponentialSmoothing</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;arima&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.arima</span><span class="w"> </span><span class="kn">import</span> <span class="n">AutoARIMA</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">AutoARIMA</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;sf-arima&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.statsforecast</span><span class="w"> </span><span class="kn">import</span> <span class="n">StatsForecastAutoARIMA</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">StatsForecastAutoARIMA</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+            <span class="n">forecaster</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="n">warnings</span><span class="o">=</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;bats&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.bats</span><span class="w"> </span><span class="kn">import</span> <span class="n">BATS</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">BATS</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;ets&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.ets</span><span class="w"> </span><span class="kn">import</span> <span class="n">AutoETS</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">AutoETS</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;croston&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.croston</span><span class="w"> </span><span class="kn">import</span> <span class="n">Croston</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">Croston</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;theta&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.theta</span><span class="w"> </span><span class="kn">import</span> <span class="n">ThetaForecaster</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">ThetaForecaster</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;unobs&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.structural</span><span class="w"> </span><span class="kn">import</span> <span class="n">UnobservedComponents</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">UnobservedComponents</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.naive</span><span class="w"> </span><span class="kn">import</span> <span class="n">NaiveForecaster</span>
+            <span class="n">forecaster</span> <span class="o">=</span> <span class="n">NaiveForecaster</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">forecaster</span></div>
+
+
+
+
 <span class="k">def</span><span class="w"> </span><span class="nf">__marshal_as_numpy_column</span><span class="p">(</span><span class="n">__ctype_container</span><span class="p">,</span> <span class="n">__py_sizen</span><span class="p">,</span> <span class="n">__py_sizem</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Marshal a ctypes container as a numpy column-major array.</span>
@@ -782,6 +861,117 @@ <h1>Source code for imputegap.tools.utils</h1><div class="highlight"><pre>
         <span class="n">seasonality_mode</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;seasonality_mode&#39;</span><span class="p">])</span>
         <span class="n">n_changepoints</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;n_changepoints&#39;</span><span class="p">])</span>
         <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;seasonality_mode&quot;</span><span class="p">:</span> <span class="n">seasonality_mode</span><span class="p">,</span> <span class="s2">&quot;n_changepoints&quot;</span><span class="p">:</span> <span class="n">n_changepoints</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-nbeats&quot;</span><span class="p">:</span>
+        <span class="n">input_chunk_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;input_chunk_length&#39;</span><span class="p">])</span>
+        <span class="n">output_chunk_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;output_chunk_length&#39;</span><span class="p">])</span>
+        <span class="n">num_blocks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;num_blocks&#39;</span><span class="p">])</span>
+        <span class="n">layer_widths</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;layer_widths&#39;</span><span class="p">])</span>
+        <span class="n">random_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;random_state&#39;</span><span class="p">])</span>
+        <span class="n">n_epochs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;n_epochs&#39;</span><span class="p">])</span>
+        <span class="n">pl_trainer_kwargs</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;pl_trainer_kwargs&#39;</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">pl_trainer_kwargs</span> <span class="o">==</span> <span class="s2">&quot;cpu&quot;</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">,</span> <span class="s2">&quot;devices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;input_chunk_length&quot;</span><span class="p">:</span> <span class="n">input_chunk_length</span><span class="p">,</span> <span class="s2">&quot;output_chunk_length&quot;</span><span class="p">:</span> <span class="n">output_chunk_length</span><span class="p">,</span> <span class="s2">&quot;num_blocks&quot;</span><span class="p">:</span> <span class="n">num_blocks</span><span class="p">,</span>
+                <span class="s2">&quot;layer_widths&quot;</span><span class="p">:</span> <span class="n">layer_widths</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="n">random_state</span><span class="p">,</span> <span class="s2">&quot;n_epochs&quot;</span><span class="p">:</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="s2">&quot;pl_trainer_kwargs&quot;</span><span class="p">:</span> <span class="n">drive</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-xgboost&quot;</span><span class="p">:</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;lags&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;lags&quot;</span><span class="p">:</span> <span class="n">lags</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-lightgbm&quot;</span><span class="p">:</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;lags&#39;</span><span class="p">])</span>
+        <span class="n">verbose</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;verbose&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;lags&quot;</span><span class="p">:</span> <span class="n">lags</span><span class="p">,</span> <span class="s2">&quot;verbose&quot;</span><span class="p">:</span> <span class="n">verbose</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-lstm&quot;</span><span class="p">:</span>
+        <span class="n">input_chunk_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;input_chunk_length&#39;</span><span class="p">])</span>
+        <span class="n">model</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">])</span>
+        <span class="n">random_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;random_state&#39;</span><span class="p">])</span>
+        <span class="n">n_epochs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;n_epochs&#39;</span><span class="p">])</span>
+        <span class="n">pl_trainer_kwargs</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;pl_trainer_kwargs&#39;</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">pl_trainer_kwargs</span> <span class="o">==</span> <span class="s2">&quot;cpu&quot;</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">,</span> <span class="s2">&quot;devices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;input_chunk_length&quot;</span><span class="p">:</span> <span class="n">input_chunk_length</span><span class="p">,</span> <span class="s2">&quot;model&quot;</span><span class="p">:</span> <span class="n">model</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="n">random_state</span><span class="p">,</span> <span class="s2">&quot;n_epochs&quot;</span><span class="p">:</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="s2">&quot;pl_trainer_kwargs&quot;</span><span class="p">:</span> <span class="n">drive</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-deepar&quot;</span><span class="p">:</span>
+        <span class="n">input_chunk_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;input_chunk_length&#39;</span><span class="p">])</span>
+        <span class="n">model</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;model&#39;</span><span class="p">])</span>
+        <span class="n">random_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;random_state&#39;</span><span class="p">])</span>
+        <span class="n">n_epochs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;n_epochs&#39;</span><span class="p">])</span>
+        <span class="n">pl_trainer_kwargs</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;pl_trainer_kwargs&#39;</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">pl_trainer_kwargs</span> <span class="o">==</span> <span class="s2">&quot;cpu&quot;</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">,</span> <span class="s2">&quot;devices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;input_chunk_length&quot;</span><span class="p">:</span> <span class="n">input_chunk_length</span><span class="p">,</span> <span class="s2">&quot;model&quot;</span><span class="p">:</span> <span class="n">model</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="n">random_state</span><span class="p">,</span> <span class="s2">&quot;n_epochs&quot;</span><span class="p">:</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="s2">&quot;pl_trainer_kwargs&quot;</span><span class="p">:</span> <span class="n">drive</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-transformer&quot;</span><span class="p">:</span>
+        <span class="n">input_chunk_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;input_chunk_length&#39;</span><span class="p">])</span>
+        <span class="n">output_chunk_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;output_chunk_length&#39;</span><span class="p">])</span>
+        <span class="n">random_state</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;random_state&#39;</span><span class="p">])</span>
+        <span class="n">n_epochs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;n_epochs&#39;</span><span class="p">])</span>
+        <span class="n">pl_trainer_kwargs</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;pl_trainer_kwargs&#39;</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">pl_trainer_kwargs</span> <span class="o">==</span> <span class="s2">&quot;cpu&quot;</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">drive</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;accelerator&quot;</span><span class="p">:</span> <span class="n">pl_trainer_kwargs</span><span class="p">,</span> <span class="s2">&quot;devices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;input_chunk_length&quot;</span><span class="p">:</span> <span class="n">input_chunk_length</span><span class="p">,</span> <span class="s2">&quot;output_chunk_length&quot;</span><span class="p">:</span> <span class="n">output_chunk_length</span><span class="p">,</span> <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="n">random_state</span><span class="p">,</span> <span class="s2">&quot;n_epochs&quot;</span><span class="p">:</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="s2">&quot;pl_trainer_kwargs&quot;</span><span class="p">:</span> <span class="n">drive</span><span class="p">}</span>
+
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-hw-add&quot;</span><span class="p">:</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;sp&#39;</span><span class="p">])</span>
+        <span class="n">trend</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;trend&#39;</span><span class="p">])</span>
+        <span class="n">seasonal</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;seasonal&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;sp&quot;</span><span class="p">:</span> <span class="n">sp</span><span class="p">,</span> <span class="s2">&quot;trend&quot;</span><span class="p">:</span> <span class="n">trend</span><span class="p">,</span> <span class="s2">&quot;seasonal&quot;</span><span class="p">:</span> <span class="n">seasonal</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-arima&quot;</span><span class="p">:</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;sp&#39;</span><span class="p">])</span>
+        <span class="n">suppress_warnings</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;suppress_warnings&#39;</span><span class="p">])</span>
+        <span class="n">start_p</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;start_p&#39;</span><span class="p">])</span>
+        <span class="n">start_q</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;start_q&#39;</span><span class="p">])</span>
+        <span class="n">max_p</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;max_p&#39;</span><span class="p">])</span>
+        <span class="n">max_q</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;max_q&#39;</span><span class="p">])</span>
+        <span class="n">start_P</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;start_P&#39;</span><span class="p">])</span>
+        <span class="n">seasonal</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;seasonal&#39;</span><span class="p">])</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;d&#39;</span><span class="p">])</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;D&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;sp&quot;</span><span class="p">:</span> <span class="n">sp</span><span class="p">,</span> <span class="s2">&quot;suppress_warnings&quot;</span><span class="p">:</span> <span class="n">suppress_warnings</span><span class="p">,</span> <span class="s2">&quot;start_p&quot;</span><span class="p">:</span> <span class="n">start_p</span><span class="p">,</span> <span class="s2">&quot;start_q&quot;</span><span class="p">:</span> <span class="n">start_q</span><span class="p">,</span>
+                <span class="s2">&quot;max_p&quot;</span><span class="p">:</span> <span class="n">max_p</span><span class="p">,</span> <span class="s2">&quot;max_q&quot;</span><span class="p">:</span> <span class="n">max_q</span><span class="p">,</span> <span class="s2">&quot;start_P&quot;</span><span class="p">:</span> <span class="n">start_P</span><span class="p">,</span> <span class="s2">&quot;seasonal&quot;</span><span class="p">:</span> <span class="n">seasonal</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">:</span> <span class="n">d</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-sf-arima&quot;</span><span class="p">:</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;sp&#39;</span><span class="p">])</span>
+        <span class="n">start_p</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;start_p&#39;</span><span class="p">])</span>
+        <span class="n">start_q</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;start_q&#39;</span><span class="p">])</span>
+        <span class="n">max_p</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;max_p&#39;</span><span class="p">])</span>
+        <span class="n">max_q</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;max_q&#39;</span><span class="p">])</span>
+        <span class="n">start_P</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;start_P&#39;</span><span class="p">])</span>
+        <span class="n">seasonal</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;seasonal&#39;</span><span class="p">])</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;d&#39;</span><span class="p">])</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;D&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;sp&quot;</span><span class="p">:</span> <span class="n">sp</span><span class="p">,</span> <span class="s2">&quot;start_p&quot;</span><span class="p">:</span> <span class="n">start_p</span><span class="p">,</span> <span class="s2">&quot;start_q&quot;</span><span class="p">:</span> <span class="n">start_q</span><span class="p">,</span>
+                <span class="s2">&quot;max_p&quot;</span><span class="p">:</span> <span class="n">max_p</span><span class="p">,</span> <span class="s2">&quot;max_q&quot;</span><span class="p">:</span> <span class="n">max_q</span><span class="p">,</span> <span class="s2">&quot;start_P&quot;</span><span class="p">:</span> <span class="n">start_P</span><span class="p">,</span> <span class="s2">&quot;seasonal&quot;</span><span class="p">:</span> <span class="n">seasonal</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">:</span> <span class="n">d</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-bats&quot;</span><span class="p">:</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;sp&#39;</span><span class="p">])</span>
+        <span class="n">use_trend</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;use_trend&#39;</span><span class="p">])</span>
+        <span class="n">use_box_cox</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;use_box_cox&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;sp&quot;</span><span class="p">:</span> <span class="n">sp</span><span class="p">,</span> <span class="s2">&quot;use_trend&quot;</span><span class="p">:</span> <span class="n">use_trend</span><span class="p">,</span> <span class="s2">&quot;use_box_cox&quot;</span><span class="p">:</span> <span class="n">use_box_cox</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-ets&quot;</span><span class="p">:</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;sp&#39;</span><span class="p">])</span>
+        <span class="n">auto</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;auto&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;sp&quot;</span><span class="p">:</span> <span class="n">sp</span><span class="p">,</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span> <span class="n">auto</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-croston&quot;</span><span class="p">:</span>
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;smoothing&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;smoothing&quot;</span><span class="p">:</span> <span class="n">smoothing</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-unobs&quot;</span><span class="p">:</span>
+        <span class="n">level</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;level&#39;</span><span class="p">])</span>
+        <span class="n">trend</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;trend&#39;</span><span class="p">])</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;sp&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;level&quot;</span><span class="p">:</span> <span class="n">level</span><span class="p">,</span> <span class="s2">&quot;trend&quot;</span><span class="p">:</span> <span class="n">trend</span><span class="p">,</span> <span class="s2">&quot;seasonal&quot;</span><span class="p">:</span> <span class="n">sp</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-theta&quot;</span><span class="p">:</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;sp&#39;</span><span class="p">])</span>
+        <span class="n">deseasonalize</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;deseasonalize&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;sp&quot;</span><span class="p">:</span> <span class="n">sp</span><span class="p">,</span> <span class="s2">&quot;deseasonalize&quot;</span><span class="p">:</span> <span class="n">deseasonalize</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;forecaster-rnn&quot;</span><span class="p">:</span>
+        <span class="n">input_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;input_size&#39;</span><span class="p">])</span>
+        <span class="n">inference_input_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;inference_input_size&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;input_size&quot;</span><span class="p">:</span> <span class="n">input_size</span><span class="p">,</span> <span class="s2">&quot;inference_input_size&quot;</span><span class="p">:</span> <span class="n">inference_input_size</span><span class="p">}</span>
+
     <span class="k">elif</span> <span class="n">algorithm</span> <span class="o">==</span> <span class="s2">&quot;colors&quot;</span><span class="p">:</span>
         <span class="n">colors</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="n">algorithm</span><span class="p">][</span><span class="s1">&#39;plot&#39;</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">colors</span>
@@ -1166,7 +1356,8 @@ <h1>Source code for imputegap.tools.utils</h1><div class="highlight"><pre>
         <span class="s2">&quot;electricity&quot;</span><span class="p">,</span>
         <span class="s2">&quot;motion&quot;</span><span class="p">,</span>
         <span class="s2">&quot;soccer&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;temperature&quot;</span>
+        <span class="s2">&quot;temperature&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;forecast-economy&quot;</span>
     <span class="p">])</span>
 
     <span class="k">if</span> <span class="n">txt</span><span class="p">:</span>
@@ -1190,13 +1381,40 @@ <h1>Source code for imputegap.tools.utils</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="list_of_downstreams">
 <a class="viewcode-back" href="../../../imputegap.utils.html#imputegap.tools.utils.list_of_downstreams">[docs]</a>
 <span class="k">def</span><span class="w"> </span><span class="nf">list_of_downstreams</span><span class="p">():</span>
+    <span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">list_of_downstreams_sktime</span><span class="p">()</span> <span class="o">+</span> <span class="n">list_of_downstreams_darts</span><span class="p">())</span></div>
+
+
+
+<div class="viewcode-block" id="list_of_downstreams_sktime">
+<a class="viewcode-back" href="../../../imputegap.utils.html#imputegap.tools.utils.list_of_downstreams_sktime">[docs]</a>
+<span class="k">def</span><span class="w"> </span><span class="nf">list_of_downstreams_sktime</span><span class="p">():</span>
     <span class="k">return</span> <span class="nb">sorted</span><span class="p">([</span>
         <span class="s2">&quot;prophet&quot;</span><span class="p">,</span>
         <span class="s2">&quot;exp-smoothing&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;hw-add&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;arima&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;sf-arima&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;bats&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;ets&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;croston&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;theta&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;unobs&quot;</span><span class="p">,</span>
         <span class="s2">&quot;naive&quot;</span>
     <span class="p">])</span></div>
 
 
+<div class="viewcode-block" id="list_of_downstreams_darts">
+<a class="viewcode-back" href="../../../imputegap.utils.html#imputegap.tools.utils.list_of_downstreams_darts">[docs]</a>
+<span class="k">def</span><span class="w"> </span><span class="nf">list_of_downstreams_darts</span><span class="p">():</span>
+    <span class="k">return</span> <span class="nb">sorted</span><span class="p">([</span>
+        <span class="s2">&quot;nbeats&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;xgboost&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;lightgbm&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;lstm&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;deepar&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;transformer&quot;</span>
+    <span class="p">])</span></div>
+
 </pre></div>
         </article>
       </div>
diff --git a/docs/generation/build/html/objects.inv b/docs/generation/build/html/objects.inv
index a29e1c57..3bf5f7a0 100644
Binary files a/docs/generation/build/html/objects.inv and b/docs/generation/build/html/objects.inv differ
diff --git a/docs/generation/build/html/searchindex.js b/docs/generation/build/html/searchindex.js
index f81a9dfe..e040a11d 100644
--- a/docs/generation/build/html/searchindex.js
+++ b/docs/generation/build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/generation/build/html/sources/downstream.rst.txt b/docs/generation/build/html/sources/downstream.rst.txt
index 76f0bf60..47f8d857 100644
--- a/docs/generation/build/html/sources/downstream.rst.txt
+++ b/docs/generation/build/html/sources/downstream.rst.txt
@@ -18,7 +18,7 @@ Below is an example of how to call the downstream process for the model Prophet
     print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}")
 
     # load and normalize the timeseries
-    ts.load_series(utils.search_path("chlorine"))
+    ts.load_series(utils.search_path("forecast-economy"))
     ts.normalize(normalizer="min_max")
 
     # contaminate the time series
@@ -29,7 +29,7 @@ Below is an example of how to call the downstream process for the model Prophet
     imputer.impute()
 
     # compute print the downstream results
-    downstream_config = {"task": "forecast", "model": "prophet"}
+    downstream_config = {"task": "forecast", "model": "hw-add"}
     imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
     ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)
 
diff --git a/docs/generation/build/html/sources/tutorials.rst.txt b/docs/generation/build/html/sources/tutorials.rst.txt
index 4a227dab..db8b4ee5 100644
--- a/docs/generation/build/html/sources/tutorials.rst.txt
+++ b/docs/generation/build/html/sources/tutorials.rst.txt
@@ -27,7 +27,7 @@ alcoholism. The dataset contains measurements from 64 electrodes placed on subje
     ts.normalize(normalizer="z_score")
 
     # plot a subset of time series
-    ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap/assets")
+    ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets")
 
     # print a subset of time series
     ts.print(nbr_series=6, nbr_val=20)
@@ -58,7 +58,7 @@ As example, we show how to contaminate the eeg-alcohol dataset with the MCAR pat
     ts_m = ts.Contamination.missing_completely_at_random(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True)
 
     # plot the contaminated time series
-    ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap/assets")
+    ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets")
 
 
 
@@ -109,7 +109,7 @@ Let's illustrate the imputation using the CDRec Algorithm from the Matrix Comple
     ts.print_results(imputer.metrics)
 
     # plot the recovered time series
-    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets")
+    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets")
 
 
 
@@ -147,7 +147,7 @@ The Optimizer component manages algorithm configuration and hyperparameter tunin
     ts.print_results(imputer.metrics)
 
     # plot the recovered time series
-    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets", display=True)
+    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True)
 
     # save hyperparameters
     utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune")
diff --git a/docs/generation/build/html/tutorials.html b/docs/generation/build/html/tutorials.html
index 8fee4b75..f555b795 100644
--- a/docs/generation/build/html/tutorials.html
+++ b/docs/generation/build/html/tutorials.html
@@ -288,7 +288,7 @@ <h1>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading
 <span class="n">ts</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">normalizer</span><span class="o">=</span><span class="s2">&quot;z_score&quot;</span><span class="p">)</span>
 
 <span class="c1"># plot a subset of time series</span>
-<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">input_data</span><span class="o">=</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">nbr_val</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap/assets&quot;</span><span class="p">)</span>
+<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">input_data</span><span class="o">=</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">nbr_val</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap_assets&quot;</span><span class="p">)</span>
 
 <span class="c1"># print a subset of time series</span>
 <span class="n">ts</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="n">nbr_series</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">nbr_val</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
@@ -314,7 +314,7 @@ <h1>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading
 <span class="n">ts_m</span> <span class="o">=</span> <span class="n">ts</span><span class="o">.</span><span class="n">Contamination</span><span class="o">.</span><span class="n">missing_completely_at_random</span><span class="p">(</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">rate_dataset</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">rate_series</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># plot the contaminated time series</span>
-<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">ts_m</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap/assets&quot;</span><span class="p">)</span>
+<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">ts_m</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap_assets&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>If you need to remove data following a specific distribution, please refer to this <a class="reference external" href="tutorials_distribution.html">tutorial</a>.</p>
@@ -349,7 +349,7 @@ <h1>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading
 <span class="n">ts</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">imputer</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span>
 
 <span class="c1"># plot the recovered time series</span>
-<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">input_data</span><span class="o">=</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">incomp_data</span><span class="o">=</span><span class="n">ts_m</span><span class="p">,</span> <span class="n">recov_data</span><span class="o">=</span><span class="n">imputer</span><span class="o">.</span><span class="n">recov_data</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap/assets&quot;</span><span class="p">)</span>
+<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">input_data</span><span class="o">=</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">incomp_data</span><span class="o">=</span><span class="n">ts_m</span><span class="p">,</span> <span class="n">recov_data</span><span class="o">=</span><span class="n">imputer</span><span class="o">.</span><span class="n">recov_data</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap_assets&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </section>
@@ -380,7 +380,7 @@ <h1>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading
 <span class="n">ts</span><span class="o">.</span><span class="n">print_results</span><span class="p">(</span><span class="n">imputer</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span>
 
 <span class="c1"># plot the recovered time series</span>
-<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">input_data</span><span class="o">=</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">incomp_data</span><span class="o">=</span><span class="n">ts_m</span><span class="p">,</span> <span class="n">recov_data</span><span class="o">=</span><span class="n">imputer</span><span class="o">.</span><span class="n">recov_data</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap/assets&quot;</span><span class="p">,</span> <span class="n">display</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">ts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">input_data</span><span class="o">=</span><span class="n">ts</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">incomp_data</span><span class="o">=</span><span class="n">ts_m</span><span class="p">,</span> <span class="n">recov_data</span><span class="o">=</span><span class="n">imputer</span><span class="o">.</span><span class="n">recov_data</span><span class="p">,</span> <span class="n">nbr_series</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;./imputegap_assets&quot;</span><span class="p">,</span> <span class="n">display</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># save hyperparameters</span>
 <span class="n">utils</span><span class="o">.</span><span class="n">save_optimization</span><span class="p">(</span><span class="n">optimal_params</span><span class="o">=</span><span class="n">imputer</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">=</span><span class="n">imputer</span><span class="o">.</span><span class="n">algorithm</span><span class="p">,</span> <span class="n">dataset</span><span class="o">=</span><span class="s2">&quot;eeg-alcohol&quot;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s2">&quot;ray_tune&quot;</span><span class="p">)</span>
diff --git a/docs/generation/report.log b/docs/generation/report.log
index 49464c9d..6b6b8a4d 100644
--- a/docs/generation/report.log
+++ b/docs/generation/report.log
@@ -62,3 +62,4 @@
 2025-03-13 16:16:46,736 - numba.cuda.cudadrv.driver - INFO - init
 2025-03-13 18:21:00,585 - numba.cuda.cudadrv.driver - INFO - init
 2025-03-14 14:37:31,196 - numba.cuda.cudadrv.driver - INFO - init
+2025-03-18 18:21:29,466 - numba.cuda.cudadrv.driver - INFO - init
diff --git a/docs/generation/source/downstream.rst b/docs/generation/source/downstream.rst
index 76f0bf60..47f8d857 100644
--- a/docs/generation/source/downstream.rst
+++ b/docs/generation/source/downstream.rst
@@ -18,7 +18,7 @@ Below is an example of how to call the downstream process for the model Prophet
     print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}")
 
     # load and normalize the timeseries
-    ts.load_series(utils.search_path("chlorine"))
+    ts.load_series(utils.search_path("forecast-economy"))
     ts.normalize(normalizer="min_max")
 
     # contaminate the time series
@@ -29,7 +29,7 @@ Below is an example of how to call the downstream process for the model Prophet
     imputer.impute()
 
     # compute print the downstream results
-    downstream_config = {"task": "forecast", "model": "prophet"}
+    downstream_config = {"task": "forecast", "model": "hw-add"}
     imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
     ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)
 
diff --git a/docs/generation/source/tutorials.rst b/docs/generation/source/tutorials.rst
index 4a227dab..db8b4ee5 100644
--- a/docs/generation/source/tutorials.rst
+++ b/docs/generation/source/tutorials.rst
@@ -27,7 +27,7 @@ alcoholism. The dataset contains measurements from 64 electrodes placed on subje
     ts.normalize(normalizer="z_score")
 
     # plot a subset of time series
-    ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap/assets")
+    ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets")
 
     # print a subset of time series
     ts.print(nbr_series=6, nbr_val=20)
@@ -58,7 +58,7 @@ As example, we show how to contaminate the eeg-alcohol dataset with the MCAR pat
     ts_m = ts.Contamination.missing_completely_at_random(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True)
 
     # plot the contaminated time series
-    ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap/assets")
+    ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets")
 
 
 
@@ -109,7 +109,7 @@ Let's illustrate the imputation using the CDRec Algorithm from the Matrix Comple
     ts.print_results(imputer.metrics)
 
     # plot the recovered time series
-    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets")
+    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets")
 
 
 
@@ -147,7 +147,7 @@ The Optimizer component manages algorithm configuration and hyperparameter tunin
     ts.print_results(imputer.metrics)
 
     # plot the recovered time series
-    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets", display=True)
+    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True)
 
     # save hyperparameters
     utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune")
diff --git a/imputegap/algorithms/__pycache__/xgboost.cpython-312.pyc b/imputegap/algorithms/__pycache__/xgboost.cpython-312.pyc
index 1092b70d..e78ef9ff 100644
Binary files a/imputegap/algorithms/__pycache__/xgboost.cpython-312.pyc and b/imputegap/algorithms/__pycache__/xgboost.cpython-312.pyc differ
diff --git a/imputegap/algorithms/xgboost.py b/imputegap/algorithms/xgboost.py
index b939eee2..29834896 100644
--- a/imputegap/algorithms/xgboost.py
+++ b/imputegap/algorithms/xgboost.py
@@ -3,7 +3,7 @@
 import numpy as np
 import pandas as pd
 
-from xgboost import XGBRegressor, XGBClassifier
+from xgboost import XGBRegressor
 
 
 def xgboost(incomp_data, n_estimators=10, seed=42, logs=True):
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deleted file mode 100644
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diff --git a/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_DTL_Beeswarm.png b/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_DTL_Beeswarm.png
deleted file mode 100644
index 519bad13..00000000
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diff --git a/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_DTL_Waterfall.png b/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_DTL_Waterfall.png
deleted file mode 100644
index 4c5fb5ce..00000000
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diff --git a/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_results.txt b/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_results.txt
deleted file mode 100644
index 85a91920..00000000
--- a/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_results.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-Feature : 5     CDRec      with a score of 31.1       Correlation        Time reversibility                                                CO_trev_1_num
-Feature : 10    CDRec      with a score of 20.85      Geometry           Goodness of exponential fit to embedding distance distribution    CO_Embed2_Dist_tau_d_expfit_meandiff
-Feature : 2     CDRec      with a score of 12.12      Correlation        First 1/e crossing of the ACF                                     CO_f1ecac
-Feature : 21    CDRec      with a score of 9.13       Trend              Error of 3-point rolling mean forecast                            FC_LocalSimple_mean3_stderr
-Feature : 8     CDRec      with a score of 5.16       Geometry           Transition matrix column variance                                 SB_TransitionMatrix_3ac_sumdiagcov
-Feature : 15    CDRec      with a score of 4.71       Transformation     Power in the lowest 20% of frequencies                            SP_Summaries_welch_rect_area_5_1
-Feature : 17    CDRec      with a score of 4.04       Trend              Entropy of successive pairs in symbolized series                  SB_MotifThree_quantile_hh
-Feature : 1     CDRec      with a score of 3.42       Geometry           10-bin histogram mode                                             DN_HistogramMode_10
-Feature : 6     CDRec      with a score of 2.09       Geometry           Proportion of high incremental changes in the series              MD_hrv_classic_pnn40
-Feature : 4     CDRec      with a score of 2.07       Correlation        Histogram-based automutual information (lag 2, 5 bins)            CO_HistogramAMI_even_2_5
-Feature : 20    CDRec      with a score of 1.61       Transformation     Centroid frequency                                                SP_Summaries_welch_rect_centroid
-Feature : 13    CDRec      with a score of 1.27       Geometry           Positive outlier timing                                           DN_OutlierInclude_p_001_mdrmd
-Feature : 0     CDRec      with a score of 1.05       Geometry           5-bin histogram mode                                              DN_HistogramMode_5
-Feature : 18    CDRec      with a score of 0.63       Geometry           Rescaled range fluctuation analysis (low-scale scaling)           SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1
-Feature : 12    CDRec      with a score of 0.41       Correlation        Change in autocorrelation timescale after incremental differencing FC_LocalSimple_mean1_tauresrat
-Feature : 14    CDRec      with a score of 0.33       Geometry           Negative outlier timing                                           DN_OutlierInclude_n_001_mdrmd
-Feature : 3     CDRec      with a score of 0.0        Correlation        First minimum of the ACF                                          CO_FirstMin_ac
-Feature : 7     CDRec      with a score of 0.0        Geometry           Longest stretch of above-mean values                              SB_BinaryStats_mean_longstretch1
-Feature : 9     CDRec      with a score of 0.0        Trend              Wangs periodicity metric                                          PD_PeriodicityWang_th0_01
-Feature : 11    CDRec      with a score of 0.0        Correlation        First minimum of the AMI function                                 IN_AutoMutualInfoStats_40_gaussian_fmmi
-Feature : 16    CDRec      with a score of 0.0        Geometry           Longest stretch of decreasing values                              SB_BinaryStats_diff_longstretch0
-Feature : 19    CDRec      with a score of 0.0        Geometry           Detrended fluctuation analysis (low-scale scaling)                SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1
diff --git a/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_aggregate_plot.png b/imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_aggregate_plot.png
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diff --git a/imputegap/dataset/README.md b/imputegap/dataset/README.md
index f34e75c9..bae717fd 100644
--- a/imputegap/dataset/README.md
+++ b/imputegap/dataset/README.md
@@ -9,8 +9,8 @@ ImputeGap uses several complete datasets containing different characteristics to
 
 This dataset, which has been sampled, defines the air quality for 10 series and 1000 values.
 
-![AIR-QUALITY dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_airq_m.jpg)
-![AIR-QUALITY dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_airq_1.jpg)
+![AIR-QUALITY dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/airq/01_airq_m.jpg)
+![AIR-QUALITY dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/airq/03_airq_1.jpg)
 
 ### Features
 | Category       | Feature                                                           | Value                 |
@@ -136,7 +136,6 @@ Chlorine dataset - raw data 20x400 provides a subset of the data, limited to 20
 Finally, Chlorine - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset.
 
 ![Chlorine dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/chlorine/01_chlorine-rawdata-NxM_graph.jpg)
-![Chlorine dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/chlorine/02_chlorine-rawdata20x400_graph.jpg)
 ![Chlorine dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/chlorine/03_chlorine-rawdata01x400_graph.jpg)
 
 ### Features
@@ -204,7 +203,6 @@ Climate dataset - raw data 20x400 provides a subset of the data, limited to 20 t
 Finally, Climate - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset.
 
 ![Climate dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/climate/01_climate-rawdata-NxM_graph.jpg)
-![Climate dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/climate/02_climate-rawdata20x400_graph.jpg)
 ![Climate dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/climate/03_climate-rawdata01x400_graph.jpg)
 
 
@@ -269,7 +267,6 @@ Drift dataset - raw data 20x400 provides a subset of the data, limited to 20 tim
 Finally, Drift - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset.
 
 ![Drift dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/drift/01_drift-rawdata-NxM_graph.jpg)
-![Drift dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/drift/02_drift-rawdata20x400_graph.jpg)
 ![Drift dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/drift/03_drift-rawdata01x400_graph.jpg)
 
 ### Features
@@ -347,7 +344,6 @@ EEG-ALCOHOL dataset - raw data 20x400 provides a subset of the data, limited to
 Finally, EEG-ALCOHOL - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset.
 
 ![EEG-ALCOHOL dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-alcohol/01_eeg-alcohol-rawdata-NxM_graph.jpg)
-![EEG-ALCOHOL dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-alcohol/02_eeg-alcohol-rawdata20x400_graph.jpg)
 ![EEG-ALCOHOL dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-alcohol/03_eeg-alcohol-rawdata01x400_graph.jpg)
 
 
@@ -492,8 +488,8 @@ Finally, EEG-READING - normalized 20x400 demonstrates the impact of "MIN-MAX" no
 This dataset records the electricity consumption of 370 individual points or clients. The data has already been normalized and reduced to a certain size.
 
 
-![ELECTRICITY dataset - raw data 20x5000](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-reading/01_electricity_M.jpg)
-![ELECTRICITY dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-reading/03_electricity_1.jpg)
+![ELECTRICITY dataset - raw data 20x5000](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/electricity/01_electricity_M.jpg)
+![ELECTRICITY dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/electricity/03_electricity_1.jpg)
 
 
 
@@ -630,7 +626,6 @@ fMRI-STOPTASK dataset - raw data 360x182 shows the full raw dataset, consisting
 fMRI-STOPTASK dataset - raw data 20x182 provides a subset of the data, limited to 20 time series over 182 time steps, while fMRI-STOPTASK dataset - raw data 01x182 focuses on a single time series extracted from the dataset.
 Finally, fMRI-STOPTASK - normalized 20x182 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x182 subset.
 
-![fMRI-STOPTASK dataset - raw data 360x182](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/fmri-stoptask/01_fmri-stoptask-rawdata-NxM_plot.jpg)
 ![fMRI-STOPTASK dataset - raw data 20x182](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/fmri-stoptask/02_fmri-stoptask-rawdata20x182_plot.jpg)
 ![fMRI-STOPTASK dataset - raw data 01x182](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/fmri-stoptask/03_fmri-stoptask-rawdata01x182_plot.jpg)
 
@@ -684,6 +679,62 @@ Finally, fMRI-STOPTASK - normalized 20x182 demonstrates the impact of "MIN-MAX"
 <br /><hr /><br />
 
 
+## FORECAST-ECONOMY
+
+This economic dataset is used for testing downstream forecasting. It exhibits a seasonality of 7 and consists of 16 time series, each containing 931 values.
+
+
+![FORECAST-ECONOMY dataset - raw data M](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/forecast-economy/forecast-economy_M.jpg)
+![FORECAST-ECONOMY dataset - raw data 1](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg)
+
+
+
+### Features
+| Category       | Feature                                                           | Value                 |
+|---------------|-------------------------------------------------------------------|-----------------------|
+| Geometry      | 5-bin histogram mode                                              | -0.5710874806115164   |
+| Geometry      | 10-bin histogram mode                                             | -0.9082987200476134   |
+| Geometry      | Proportion of high incremental changes in the series              | 0.7816717019133937    |
+| Geometry      | Longest stretch of above-mean values                              | 357.0                 |
+| Geometry      | Transition matrix column variance                                 | 0.011316872427983538  |
+| Geometry      | Goodness of exponential fit to embedding distance distribution    | 0.12664898312226522   |
+| Geometry      | Positive outlier timing                                           | 0.18958109559613323   |
+| Geometry      | Negative outlier timing                                           | -0.2299274973147154   |
+| Geometry      | Longest stretch of decreasing values                              | 7.0                   |
+| Geometry      | Rescaled range fluctuation analysis (low-scale scaling)           | 0.3                   |
+| Geometry      | Detrended fluctuation analysis (low-scale scaling)                | 0.22                  |
+| Correlation   | First 1/e crossing of the ACF                                     | 124.60446764082629    |
+| Correlation   | First minimum of the ACF                                          | 1                     |
+| Correlation   | Histogram-based automutual information (lag 2, 5 bins)            | 0.22074051585149523   |
+| Correlation   | Time reversibility                                                | 0.28049126008447584   |
+| Correlation   | First minimum of the AMI function                                 | 5.0                   |
+| Correlation   | Change in autocorrelation timescale after incremental differencing| 0.0012224938875305623 |
+| Trend         | Wangs periodicity metric                                          | 6                     |
+| Trend         | Entropy of successive pairs in symbolized series                  | 1.8906454432766748    |
+| Trend         | Error of 3-point rolling mean forecast                            | 0.7191953107910503    |
+| Transformation| Power in the lowest 20% of frequencies                            | 0.6678786769493903    |
+| Transformation| Centroid frequency                                                | 0.009203884727314454  |
+
+
+
+
+### Summary
+
+| Data info          |                                                                                          |
+|--------------------|------------------------------------------------------------------------------------------|
+| Dataset codename   | forecast-economy                                                                         |
+| Dataset name       | ECONOMY                                                                              |
+| Dataset source     | https://zenodo.org/records/14023107                                                      | 
+| Dataset dimensions | M=16 N=931                                                                               |
+
+
+
+
+
+<br /><hr /><br />
+
+
+
 
 
 
@@ -699,9 +750,7 @@ Meteo dataset - raw data 20x400 provides a subset of the data, limited to 20 tim
 Finally, Meteo - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset.
 
 ![Meteo dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_meteo-rawdata-NxM_graph.jpg)
-![Meteo dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/02_meteo-rawdata20x400_graph.jpg)
 ![Meteo dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_meteo-rawdata01x400_graph.jpg)
-![Meteo - normalized 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/04_meteo-normmin_maxdata01x400_graph.jpg)
 
 ### Features
 
@@ -774,8 +823,8 @@ Example: 13 = observation period 12:41 to 13:40
 
 This dataset consists of time series data collected from accelerometer and gyroscope sensors, capturing attributes such as attitude, gravity, user acceleration, and rotation rate [[4]](#ref4). Recorded at a high sampling rate of 50Hz using an iPhone 6s placed in users' front pockets, the data reflects various human activities. While the motion time series are non-periodic, they display partial trend similarities.
 
-![Motion dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_motion_M.jpg)
-![Motion dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_motion_1.jpg)
+![Motion dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/motion/01_motion_M.jpg)
+![Motion dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/motion/03_motion_1.jpg)
 
 ### Features
 | Category       | Feature                                                           | Value                 |
@@ -824,8 +873,35 @@ This dataset consists of time series data collected from accelerometer and gyros
 
 This dataset, initially presented in the DEBS Challenge 2013 [[3]](#ref3), captures player positions during a football match. The data is collected from sensors placed near players' shoes and the goalkeeper's hands. With a high tracking frequency of 200Hz, it generates 15,000 position events per second. Soccer time series exhibit bursty behavior and contain numerous outliers.
 
-![Soccer dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_soccer_M.jpg)
-![Soccer dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_soccer_1.jpg)
+![Soccer dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/soccer/01_soccer_M.jpg)
+![Soccer dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/soccer/03_soccer_1.jpg)
+
+### Features
+| Category       | Feature                                                           | Value                 |
+|---------------|-------------------------------------------------------------------|-----------------------|
+| Geometry      | 5-bin histogram mode                                              | 0.09084722786947164   |
+| Geometry      | 10-bin histogram mode                                             | -0.2583118928950434   |
+| Geometry      | Proportion of high incremental changes in the series              | 0.0011092863312203406 |
+| Geometry      | Longest stretch of above-mean values                              | 71757.0               |
+| Geometry      | Transition matrix column variance                                 | 0.006802721088435373  |
+| Geometry      | Goodness of exponential fit to embedding distance distribution    | 0.3417367925024475    |
+| Geometry      | Positive outlier timing                                           | 0.1377957597962023    |
+| Geometry      | Negative outlier timing                                           | 0.05898850648030396   |
+| Geometry      | Longest stretch of decreasing values                              | 1096.0                |
+| Geometry      | Rescaled range fluctuation analysis (low-scale scaling)           | 0.48                  |
+| Geometry      | Detrended fluctuation analysis (low-scale scaling)                | 0.46                  |
+| Correlation   | First 1/e crossing of the ACF                                     | 17792.5919437391      |
+| Correlation   | First minimum of the ACF                                          | 5221                  |
+| Correlation   | Histogram-based automutual information (lag 2, 5 bins)            | 1.1086843892176654    |
+| Correlation   | Time reversibility                                                | 6.552378315312122e-06 |
+| Correlation   | First minimum of the AMI function                                 | 40.0                  |
+| Correlation   | Change in autocorrelation timescale after incremental differencing| 0.0006084989404959624 |
+| Trend         | Wangs periodicity metric                                          | 11198                 |
+| Trend         | Entropy of successive pairs in symbolized series                  | 1.1035630861406998    |
+| Trend         | Error of 3-point rolling mean forecast                            | 0.01179174759304637   |
+| Transformation| Power in the lowest 20% of frequencies                            | 0.9999572395164824    |
+| Transformation| Centroid frequency                                                | 0.0001370695723550248 |
+
 
 
 ### Summary
@@ -849,8 +925,35 @@ This dataset, initially presented in the DEBS Challenge 2013 [[3]](#ref3), captu
 
 ## Temperature
 
-![Temperature dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_temperature_20.jpg)
-![Temperature dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_temperature_1.jpg)
+![Temperature dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/temperature/01_temperature_20.jpg)
+![Temperature dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/temperature/03_temperature_1.jpg)
+
+### Features
+| Category       | Feature                                                           | Value                 |
+|---------------|-------------------------------------------------------------------|-----------------------|
+| Geometry      | 5-bin histogram mode                                              | 22.045650551725167    |
+| Geometry      | 10-bin histogram mode                                             | 8.743492676833597     |
+| Geometry      | Proportion of high incremental changes in the series              | 0.7958665687091343    |
+| Geometry      | Longest stretch of above-mean values                              | 21931.0               |
+| Geometry      | Transition matrix column variance                                 | 0.0008670367268468369 |
+| Geometry      | Goodness of exponential fit to embedding distance distribution    | 0.0037844314057919114 |
+| Geometry      | Positive outlier timing                                           | 0.4030755233654515    |
+| Geometry      | Negative outlier timing                                           | -0.572629720089644    |
+| Geometry      | Longest stretch of decreasing values                              | 15.0                  |
+| Geometry      | Rescaled range fluctuation analysis (low-scale scaling)           | 0.4                   |
+| Geometry      | Detrended fluctuation analysis (low-scale scaling)                | 0.38                  |
+| Correlation   | First 1/e crossing of the ACF                                     | 81.7405995576158      |
+| Correlation   | First minimum of the ACF                                          | 183                   |
+| Correlation   | Histogram-based automutual information (lag 2, 5 bins)            | 1.439425719697347e-06 |
+| Correlation   | Time reversibility                                                | 0.005588686797775345  |
+| Correlation   | First minimum of the AMI function                                 | 40.0                  |
+| Correlation   | Change in autocorrelation timescale after incremental differencing| 0.00847457627118644   |
+| Trend         | Wangs periodicity metric                                          | 365                   |
+| Trend         | Entropy of successive pairs in symbolized series                  | 1.4530196005684877    |
+| Trend         | Error of 3-point rolling mean forecast                            | 0.3145083996285715    |
+| Transformation| Power in the lowest 20% of frequencies                            | 0.9542560901714987    |
+| Transformation| Centroid frequency                                                | 0.017202605837583908  |
+
 
 
 ### Summary
diff --git a/imputegap/dataset/docs/forecast-economy/features_forecast-economy.txt b/imputegap/dataset/docs/forecast-economy/features_forecast-economy.txt
new file mode 100644
index 00000000..6e5b1a70
--- /dev/null
+++ b/imputegap/dataset/docs/forecast-economy/features_forecast-economy.txt
@@ -0,0 +1,22 @@
+|Geometry|5-bin histogram mode|-0.5710874806115164|
+|Geometry|10-bin histogram mode|-0.9082987200476134|
+|Correlation|First 1/e crossing of the ACF|124.60446764082629|
+|Correlation|First minimum of the ACF|1|
+|Correlation|Histogram-based automutual information (lag 2, 5 bins)|0.22074051585149523|
+|Correlation|Time reversibility|0.28049126008447584|
+|Geometry|Proportion of high incremental changes in the series|0.7816717019133937|
+|Geometry|Longest stretch of above-mean values|357.0|
+|Geometry|Transition matrix column variance|0.011316872427983538|
+|Trend|Wangs periodicity metric|6|
+|Geometry|Goodness of exponential fit to embedding distance distribution|0.12664898312226522|
+|Correlation|First minimum of the AMI function|5.0|
+|Correlation|Change in autocorrelation timescale after incremental differencing|0.0012224938875305623|
+|Geometry|Positive outlier timing|0.18958109559613323|
+|Geometry|Negative outlier timing|-0.2299274973147154|
+|Transformation|Power in the lowest 20% of frequencies|0.6678786769493903|
+|Geometry|Longest stretch of decreasing values|7.0|
+|Trend|Entropy of successive pairs in symbolized series|1.8906454432766748|
+|Geometry|Rescaled range fluctuation analysis (low-scale scaling)|0.3|
+|Geometry|Detrended fluctuation analysis (low-scale scaling)|0.22|
+|Transformation|Centroid frequency|0.009203884727314454|
+|Trend|Error of 3-point rolling mean forecast|0.7191953107910503|
diff --git a/imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg b/imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg
new file mode 100644
index 00000000..496e58f8
Binary files /dev/null and b/imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg differ
diff --git a/imputegap/dataset/docs/forecast-economy/forecast-economy_M.jpg b/imputegap/dataset/docs/forecast-economy/forecast-economy_M.jpg
new file mode 100644
index 00000000..2f8e330b
Binary files /dev/null and b/imputegap/dataset/docs/forecast-economy/forecast-economy_M.jpg differ
diff --git a/imputegap/dataset/docs/soccer/features_soccer.txt b/imputegap/dataset/docs/soccer/features_soccer.txt
new file mode 100644
index 00000000..24f24056
--- /dev/null
+++ b/imputegap/dataset/docs/soccer/features_soccer.txt
@@ -0,0 +1,22 @@
+|Geometry|5-bin histogram mode|0.09084722786947164|
+|Geometry|10-bin histogram mode|-0.2583118928950434|
+|Correlation|First 1/e crossing of the ACF|17792.5919437391|
+|Correlation|First minimum of the ACF|5221|
+|Correlation|Histogram-based automutual information (lag 2, 5 bins)|1.1086843892176654|
+|Correlation|Time reversibility|6.552378315312122e-06|
+|Geometry|Proportion of high incremental changes in the series|0.0011092863312203406|
+|Geometry|Longest stretch of above-mean values|71757.0|
+|Geometry|Transition matrix column variance|0.006802721088435373|
+|Trend|Wangs periodicity metric|11198|
+|Geometry|Goodness of exponential fit to embedding distance distribution|0.3417367925024475|
+|Correlation|First minimum of the AMI function|40.0|
+|Correlation|Change in autocorrelation timescale after incremental differencing|0.0006084989404959624|
+|Geometry|Positive outlier timing|0.1377957597962023|
+|Geometry|Negative outlier timing|0.05898850648030396|
+|Transformation|Power in the lowest 20% of frequencies|0.9999572395164824|
+|Geometry|Longest stretch of decreasing values|1096.0|
+|Trend|Entropy of successive pairs in symbolized series|1.1035630861406998|
+|Geometry|Rescaled range fluctuation analysis (low-scale scaling)|0.48|
+|Geometry|Detrended fluctuation analysis (low-scale scaling)|0.46|
+|Transformation|Centroid frequency|0.0001370695723550248|
+|Trend|Error of 3-point rolling mean forecast|0.01179174759304637|
diff --git a/imputegap/dataset/docs/temperature/features_temperature.txt b/imputegap/dataset/docs/temperature/features_temperature.txt
new file mode 100644
index 00000000..4a8c3eda
--- /dev/null
+++ b/imputegap/dataset/docs/temperature/features_temperature.txt
@@ -0,0 +1,22 @@
+|Geometry|5-bin histogram mode|22.045650551725167|
+|Geometry|10-bin histogram mode|8.743492676833597|
+|Correlation|First 1/e crossing of the ACF|81.7405995576158|
+|Correlation|First minimum of the ACF|183|
+|Correlation|Histogram-based automutual information (lag 2, 5 bins)|1.439425719697347e-06|
+|Correlation|Time reversibility|0.005588686797775345|
+|Geometry|Proportion of high incremental changes in the series|0.7958665687091343|
+|Geometry|Longest stretch of above-mean values|21931.0|
+|Geometry|Transition matrix column variance|0.0008670367268468369|
+|Trend|Wangs periodicity metric|365|
+|Geometry|Goodness of exponential fit to embedding distance distribution|0.0037844314057919114|
+|Correlation|First minimum of the AMI function|40.0|
+|Correlation|Change in autocorrelation timescale after incremental differencing|0.00847457627118644|
+|Geometry|Positive outlier timing|0.4030755233654515|
+|Geometry|Negative outlier timing|-0.572629720089644|
+|Transformation|Power in the lowest 20% of frequencies|0.9542560901714987|
+|Geometry|Longest stretch of decreasing values|15.0|
+|Trend|Entropy of successive pairs in symbolized series|1.4530196005684877|
+|Geometry|Rescaled range fluctuation analysis (low-scale scaling)|0.4|
+|Geometry|Detrended fluctuation analysis (low-scale scaling)|0.38|
+|Transformation|Centroid frequency|0.017202605837583908|
+|Trend|Error of 3-point rolling mean forecast|0.3145083996285715|
diff --git a/imputegap/dataset/forecast-economy.txt b/imputegap/dataset/forecast-economy.txt
new file mode 100644
index 00000000..ca1a33b2
--- /dev/null
+++ b/imputegap/dataset/forecast-economy.txt
@@ -0,0 +1,931 @@
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+79.025324 525.485947 418.672452 55.896716 1070.051358 928.430580 133.066308 795.484208 537.991157 767.245797 818.544238 748.793147 1804.191997 1178.575384 146.507883 427.126543
+78.205641 578.341225 495.196187 57.288741 1013.875616 981.223981 124.432637 840.795871 523.756219 742.463520 825.760037 703.399296 1587.373346 1106.859388 139.986498 395.466652
+76.224740 526.172379 280.241877 37.468959 800.163553 836.867026 99.905161 819.882796 486.033632 752.226236 928.224385 731.932574 2074.870058 1065.152212 96.510602 292.948911
+11.606375 12.721107 15.418166 12.213653 6.375891 10.320343 60.268760 7.758389 6.316212 376.737193 8.931571 5.630960 1371.935706 717.253334 5.211219 11.025124
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+70.077115 455.469865 412.653732 51.985789 852.675660 852.540067 120.312021 638.636147 473.933935 688.393098 692.989332 634.660036 1659.185893 923.500403 122.793757 352.122755
+77.317651 470.571373 392.018118 54.703552 795.278706 949.877899 133.262528 727.516715 515.927003 606.536487 760.817844 634.660036 1620.517598 844.155045 116.272373 346.092299
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+68.574363 498.028660 520.130887 51.720642 913.736249 968.025631 120.312021 711.831909 485.321885 671.871580 755.045205 587.969218 1498.988673 889.168277 118.841403 371.721734
+83.601889 546.078913 321.513104 38.927271 801.384765 950.702796 100.788150 849.509652 535.144169 659.104953 945.542303 577.593481 1585.992336 843.137797 90.977306 325.362609
+11.606375 12.721107 15.418166 12.213653 6.375891 10.320343 67.627003 7.758389 6.316212 407.527295 8.931571 5.630960 1323.600338 683.429832 5.211219 11.025124
+93.096554 629.137207 644.804387 64.978020 1211.711926 1136.304596 154.552377 1124.865137 658.988132 870.880773 1141.812040 722.853804 1859.432418 844.409357 175.360069 413.934922
+86.948929 548.824641 559.682480 59.675069 1117.678618 986.173363 130.319231 858.223433 597.777898 703.412660 935.440184 711.181099 1769.666734 853.055966 148.681678 397.351170
+88.724910 522.053786 494.336370 53.178953 977.239262 925.130993 133.262528 791.998696 619.842052 713.175375 799.783160 718.962902 1630.184672 838.814492 133.860349 422.603701
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+81.689295 541.960320 490.897101 50.063469 1026.087734 879.761664 134.047407 912.248876 549.379108 743.965477 835.862156 678.756920 1747.570566 1043.281376 137.219850 367.575796
+86.744009 579.027657 338.709449 37.800394 779.402953 821.193985 106.772854 886.107533 563.614046 770.249710 900.804348 669.678150 1815.240081 814.400536 100.067720 346.092299
+11.606375 12.721107 15.418166 12.213653 6.375891 10.320343 88.131973 7.758389 6.316212 564.481714 8.931571 5.630960 1508.655747 732.003433 5.211219 11.025124
+77.249344 504.206550 421.251904 50.063469 960.142297 844.291098 148.960113 774.571134 559.343564 905.425765 801.226320 687.835690 2092.823195 845.935229 129.710377 382.275031
+79.093631 438.995492 419.532270 48.804018 853.896872 756.027131 135.617166 621.208585 450.446286 828.075022 718.966209 613.908562 1939.531027 815.926408 106.786723 349.484430
+74.858601 415.656798 379.980677 43.302206 795.278706 669.412957 110.599140 673.491272 530.161941 746.218411 701.648291 606.126759 1891.195659 800.667685 102.241515 341.192554
+74.995215 473.317101 379.980677 50.328617 873.436260 771.700171 152.393959 765.857352 482.474898 742.463520 746.386246 658.005446 1905.005765 809.822919 120.224727 359.660824
+74.721987 513.816600 438.448249 47.080559 901.524131 795.622181 124.138307 734.487740 466.816465 792.779052 753.602045 600.938890 1772.428755 976.142996 112.715254 355.514886
+81.347760 564.612582 274.223156 35.878074 605.990879 843.466201 101.671139 840.795871 456.852009 827.324044 840.191635 654.114544 1826.288165 743.956099 76.155977 218.322026
+11.606375 12.721107 15.418166 12.213653 6.375891 10.320343 94.411006 7.758389 6.316212 400.017514 8.931571 5.630960 1602.564462 721.068015 5.211219 11.025124
diff --git a/imputegap/env/default_values.toml b/imputegap/env/default_values.toml
index a7112c7e..e422936d 100644
--- a/imputegap/env/default_values.toml
+++ b/imputegap/env/default_values.toml
@@ -209,17 +209,111 @@ max_concurrent_trials = -1
 [forecaster-naive]
 strategy = "mean"
 window_length = 10
-sp = 5
+sp = 7
 
 [forecaster-exp-smoothing]
 trend = "additive"
 seasonal = "additive"
-sp = 5
+sp = 7
 
 [forecaster-prophet]
 seasonality_mode = "additive"
 n_changepoints = 25
 
+[forecaster-nbeats]
+input_chunk_length = 12
+output_chunk_length = 12
+num_blocks = 3
+layer_widths = 20
+random_state = 42
+n_epochs = 10
+pl_trainer_kwargs = "cpu"
+
+[forecaster-xgboost]
+lags = 7
+
+[forecaster-lightgbm]
+lags = 7
+verbose = -1
+
+[forecaster-lstm]
+input_chunk_length = 12
+model = 'LSTM'
+random_state = 42
+n_epochs = 10
+pl_trainer_kwargs = "cpu"
+
+[forecaster-deepar]
+input_chunk_length = 12
+model = 'RNN'
+random_state = 42
+n_epochs = 10
+pl_trainer_kwargs = "cpu"
+
+[forecaster-transformer]
+input_chunk_length = 12
+output_chunk_length = 12
+random_state = 42
+n_epochs = 10
+pl_trainer_kwargs = "cpu"
+
+[forecaster-hw-add]
+sp = 7
+trend = "add"
+seasonal = "additive"
+
+[forecaster-arima]
+sp = 7
+suppress_warnings = true
+start_p = 1
+start_q = 1
+max_p = 3
+max_q = 3
+start_P = 0
+seasonal = true
+d = 1
+D = 1
+
+[forecaster-sf-arima]
+sp= 7
+start_p = 1
+start_q = 1
+max_p = 3
+max_q = 3
+start_P = 0
+seasonal = true
+d = 1
+D = 1
+
+[forecaster-bats]
+sp= 7
+use_trend = true
+use_box_cox = false
+
+
+[forecaster-ets]
+sp= 7
+auto = true
+
+[forecaster-croston]
+smoothing = 0.1
+
+[forecaster-theta]
+sp = 7
+deseasonalize = false
+
+[forecaster-unobs]
+level = true
+trend = true
+sp = 7
+
+[forecaster-rnn]
+input_size = 20
+inference_input_size = 12
+
+
+
+
 
 
 
diff --git a/imputegap/assets/shap/.gitkeep b/imputegap/imputegap_assets/.gitkeep
similarity index 100%
rename from imputegap/assets/shap/.gitkeep
rename to imputegap/imputegap_assets/.gitkeep
diff --git a/imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg b/imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg
new file mode 100644
index 00000000..e0560938
Binary files /dev/null and b/imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg differ
diff --git a/imputegap/imputegap_assets/shap/.gitkeep b/imputegap/imputegap_assets/shap/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc b/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc
index 05509dd6..6eb0c663 100644
Binary files a/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc and b/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc differ
diff --git a/imputegap/recovery/__pycache__/downstream.cpython-312.pyc b/imputegap/recovery/__pycache__/downstream.cpython-312.pyc
index eb4b020f..86f4b575 100644
Binary files a/imputegap/recovery/__pycache__/downstream.cpython-312.pyc and b/imputegap/recovery/__pycache__/downstream.cpython-312.pyc differ
diff --git a/imputegap/recovery/__pycache__/explainer.cpython-312.pyc b/imputegap/recovery/__pycache__/explainer.cpython-312.pyc
index b7935b13..1d5bfae7 100644
Binary files a/imputegap/recovery/__pycache__/explainer.cpython-312.pyc and b/imputegap/recovery/__pycache__/explainer.cpython-312.pyc differ
diff --git a/imputegap/recovery/__pycache__/imputation.cpython-312.pyc b/imputegap/recovery/__pycache__/imputation.cpython-312.pyc
index abdad1dd..71bb4bdf 100644
Binary files a/imputegap/recovery/__pycache__/imputation.cpython-312.pyc and b/imputegap/recovery/__pycache__/imputation.cpython-312.pyc differ
diff --git a/imputegap/recovery/__pycache__/manager.cpython-312.pyc b/imputegap/recovery/__pycache__/manager.cpython-312.pyc
index 42f5d3ef..f1f5f278 100644
Binary files a/imputegap/recovery/__pycache__/manager.cpython-312.pyc and b/imputegap/recovery/__pycache__/manager.cpython-312.pyc differ
diff --git a/imputegap/recovery/benchmark.py b/imputegap/recovery/benchmark.py
index 71b47e64..e25cc722 100644
--- a/imputegap/recovery/benchmark.py
+++ b/imputegap/recovery/benchmark.py
@@ -121,10 +121,13 @@ def average_runs_by_names(self, data):
 
                                     # Add scores and times
                                     for score_key, v in level_value["scores"].items():
+                                        if v is None :
+                                            v = 0
                                         merger["scores"][score_key] = (merger["scores"].get(score_key, 0) + v / count)
                                     for time_key, time_value in level_value["times"].items():
-                                        merger["times"][time_key] = (
-                                                    merger["times"].get(time_key, 0) + time_value / count)
+                                        if time_value is None :
+                                            time_value = 0
+                                        merger["times"][time_key] = (merger["times"].get(time_key, 0) + time_value / count)
 
             results_avg.append(merged_dict)
 
diff --git a/imputegap/recovery/downstream.py b/imputegap/recovery/downstream.py
index 2bb55136..eafbddca 100644
--- a/imputegap/recovery/downstream.py
+++ b/imputegap/recovery/downstream.py
@@ -6,10 +6,15 @@
 
 from imputegap.tools import utils
 
-from sktime.forecasting.exp_smoothing import ExponentialSmoothing
-from sktime.forecasting.fbprophet import Prophet
-from sktime.forecasting.naive import NaiveForecaster
-from sktime.performance_metrics.forecasting import mean_absolute_error, mean_squared_error
+from darts import TimeSeries
+from darts.metrics import mae as darts_mae, mse as darts_mse
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from sktime.forecasting.base import ForecastingHorizon
+
+
+
+
+
 
 class Downstream:
     """
@@ -19,6 +24,10 @@ class Downstream:
     the performance of downstream forecasting models. It computes metrics such as Mean Absolute
     Error (MAE) and Mean Squared Error (MSE) and visualizes the results for better interpretability.
 
+    ImputeGAP downstream models for forcasting : ['arima', 'bats', 'croston', 'deepar', 'ets', 'exp-smoothing',
+    'hw-add', 'lightgbm', 'lstm', 'naive', 'nbeats', 'prophet', 'sf-arima', 'theta',
+    'transformer', 'unobs', 'xgboost']
+
     Attributes
     ----------
     input_data : numpy.ndarray
@@ -64,11 +73,16 @@ def __init__(self, input_data, recov_data, incomp_data, downstream):
         self.incomp_data = incomp_data
         self.downstream = downstream
         self.split = 0.8
+        self.sktime_models = utils.list_of_downstreams_sktime()
 
     def downstream_analysis(self):
         """
         Compute a set of evaluation metrics with a downstream analysis
 
+        ImputeGAP downstream models for forcasting : ['arima', 'bats', 'croston', 'deepar', 'ets', 'exp-smoothing',
+        'hw-add', 'lightgbm', 'lstm', 'naive', 'nbeats', 'prophet', 'sf-arima', 'theta',
+        'transformer', 'unobs', 'xgboost']
+
         Returns
         -------
         dict or None
@@ -78,6 +92,9 @@ def downstream_analysis(self):
         model = self.downstream.get("model", "naive")
         params = self.downstream.get("params", None)
         plots = self.downstream.get("plots", True)
+        
+        model = model.lower()
+        evaluator = evaluator.lower()
 
         if not params:
             print("\n\t\t\t\tThe params for model of downstream analysis are empty or missing. Default ones loaded...")
@@ -85,9 +102,9 @@ def downstream_analysis(self):
             params = utils.load_parameters(query="default", algorithm=loader)
 
         print("\n\t\t\t\tDownstream analysis launched for <", evaluator, "> on the model <", model,
-              "> with parameters :\n\t\t\t\t\t", params)
+              "> with parameters :\n\t\t\t\t\t", params, " \n\n")
 
-        if evaluator == "forecast" or evaluator == "forecaster"or evaluator == "forecasting":
+        if evaluator in ["forecast", "forecaster", "forecasting"]:
             y_train_all, y_test_all, y_pred_all = [], [], []
             mae, mse = [], []
 
@@ -106,35 +123,63 @@ def downstream_analysis(self):
 
                 y_train = data[:, :train_len]
                 y_test = data[:, train_len:]
-                y_pred = np.zeros_like(y_test)
 
-                # Forecast for each series
-                for series_idx in range(data.shape[0]):
-                    series_train = y_train[series_idx, :]
+                forecaster = utils.config_forecaster(model, params)
+
+                if model in self.sktime_models:
+                    # --- SKTIME APPROACH ---
+                    y_pred = np.zeros_like(y_test)
+
+                    for series_idx in range(data.shape[0]):
+                        series_train = y_train[series_idx, :]
+                        fh = np.arange(1, y_test.shape[1] + 1)  # Forecast horizon
 
-                    # Initialize and fit the forecasting model
-                    if model == "prophet":
-                        forecaster = Prophet(**params)
-                    elif model == "exp-smoothing":
-                        forecaster = ExponentialSmoothing(**params)
-                    else:
-                        forecaster = NaiveForecaster(**params)
+                        if model == "ltsf" or model == "rnn":
+                            forecaster.fit(series_train, fh=ForecastingHorizon(fh))
+                            series_pred = forecaster.predict()
+                        else:
+                            forecaster.fit(series_train)
+                            series_pred = forecaster.predict(fh=fh)
 
-                    forecaster.fit(series_train)
-                    fh = np.arange(1, y_test.shape[1] + 1)  # Forecast horizon
-                    series_pred = forecaster.predict(fh=fh)
-                    series_pred = series_pred.ravel()
+                        y_pred[series_idx, :] = series_pred.ravel()
 
-                    # Store predictions
-                    y_pred[series_idx, :] = series_pred
+                    # Compute metrics using sktime
+                    mae.append(mean_absolute_error(y_test, y_pred))
+                    mse.append(mean_squared_error(y_test, y_pred))
+
+                else:
+                    # --- DARTS APPROACH ---
+                    # Convert entire matrix to a Darts multivariate TimeSeries object
+                    y_train_ts = TimeSeries.from_values(y_train.T)  # Shape: (time_steps, n_series)
+                    y_test_ts = TimeSeries.from_values(y_test.T)  # Shape: (time_steps, n_series)
 
-                # Validate shapes
-                if y_pred.shape != y_test.shape:
-                    raise ValueError(f"Shape mismatch: y_pred={y_pred.shape}, y_test={y_test.shape}, y_train={y_train.shape}")
+                    # Fit the model
+                    forecaster.fit(y_train_ts)
 
-                # Calculate metrics
-                mae.append(mean_absolute_error(y_test, y_pred))
-                mse.append(mean_squared_error(y_test, y_pred))
+                    # Predict for the entire series at once
+                    forecast_horizon = y_test.shape[1]
+                    y_pred_ts = forecaster.predict(n=forecast_horizon)
+
+                    # Convert predictions back to NumPy
+                    y_pred = y_pred_ts.values().T  # Shape: (n_series, time_steps)
+
+
+
+                    # Ensure y_pred_ts has the same components as y_test_ts
+                    y_pred_ts = y_pred_ts.with_columns_renamed(y_pred_ts.components, y_test_ts.components)
+
+                    # Shift time index to match
+                    if y_pred_ts.start_time() != y_test_ts.start_time():
+                        y_pred_ts = y_pred_ts.shift(y_test_ts.start_time() - y_pred_ts.start_time())
+
+                    # Compute metrics safely
+                    mae_score = darts_mae(y_test_ts, y_pred_ts)
+                    mse_score = darts_mse(y_test_ts, y_pred_ts)
+
+
+                    # Compute metrics using Darts
+                    mae.append(mae_score)
+                    mse.append(mse_score)
 
                 # Store for plotting
                 y_train_all.append(y_train)
@@ -143,14 +188,14 @@ def downstream_analysis(self):
 
             if plots:
                 # Global plot with all rows and columns
-                self._plot_downstream(y_train_all, y_test_all, y_pred_all, self.incomp_data)
+                self._plot_downstream(y_train_all, y_test_all, y_pred_all, self.incomp_data, model, evaluator)
 
             # Save metrics in a dictionary
             metrics = {"DOWNSTREAM-RECOV-MAE": mae[0], "DOWNSTREAM-INPUT-MAE": mae[1],
                        "DOWNSTREAM-MEANI-MAE": mae[2], "DOWNSTREAM-RECOV-MSE": mse[0],
                        "DOWNSTREAM-INPUT-MSE": mse[1], "DOWNSTREAM-MEANI-MSE": mse[2]}
 
-            print("\t\t\t\tDownstream analysis complete. " + "*" * 58 + "\n")
+            print("\n\t\t\t\tDownstream analysis complete. " + "*" * 58 + "\n")
 
             return metrics
         else:
@@ -159,7 +204,7 @@ def downstream_analysis(self):
             return None
 
     @staticmethod
-    def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth vs Predictions", max_series=4, save_path="./imputegap/assets"):
+    def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None, title="Ground Truth vs Predictions", max_series=1, save_path="./imputegap_assets"):
         """
         Plot ground truth vs. predictions for contaminated series (series with NaN values).
 
@@ -173,6 +218,10 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v
             Forecasted data array of shape (n_series, test_len).
         incomp_data : np.ndarray
             Incomplete data array of shape (n_series, total_len), used to identify contaminated series.
+        model : str
+            Name of the current model used
+        type : str
+            Name of the current type used
         title : str
             Title of the plot.
         max_series : int
@@ -182,6 +231,9 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v
 
         x_size = max_series * 5
 
+        if max_series == 1:
+            x_size = 24
+
         fig, axs = plt.subplots(3, max_series, figsize=(x_size, 15))
         fig.suptitle(title, fontsize=16)
 
@@ -192,7 +244,10 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v
 
             for col_idx, series_idx in enumerate(valid_indices):
                 # Access the correct subplot
-                ax = axs[row_idx, col_idx]
+                if max_series > 1:
+                    ax = axs[row_idx, col_idx]
+                else:
+                    ax = axs[row_idx]
 
                 # Extract the corresponding data for this data type and series
                 s_y_train = y_train[row_idx]
@@ -247,7 +302,7 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v
 
             now = datetime.datetime.now()
             current_time = now.strftime("%y_%m_%d_%H_%M_%S")
-            file_path = os.path.join(save_path + "/" + current_time + "_downstream.jpg")
+            file_path = os.path.join(save_path + "/" + current_time + "_" + type + "_" + model + "_downstream.jpg")
             plt.savefig(file_path, bbox_inches='tight')
             print("plots saved in ", file_path)
 
diff --git a/imputegap/recovery/explainer.py b/imputegap/recovery/explainer.py
index 01714076..efb4c1c2 100644
--- a/imputegap/recovery/explainer.py
+++ b/imputegap/recovery/explainer.py
@@ -409,7 +409,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl
         _, _, config = Explainer.load_configuration()
         plots_categories = config[extractor]['categories']
 
-        path_file = "./assets/shap/"
+        path_file = "./imputegap_assets/shap/"
         if not os.path.exists(path_file):
             path_file = "./imputegap" + path_file[1:]
 
@@ -635,7 +635,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl
         return results_shap
 
     def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatch", pattern="mcar", missing_rate=0.4,
-                       block_size=10, offset=0.1, seed=True, limit_ratio=1, split_ratio=0.6,
+                       block_size=10, offset=0.1, seed=True, rate_dataset=1, training_ratio=0.6,
                        file_name="ts", display=False, verbose=False):
         """
         Handle parameters and set variables to launch the SHAP model.
@@ -660,9 +660,9 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc
             Size of the uncontaminated section at the beginning of the time series (default is 0.1).
         seed : bool, optional
             Whether to use a seed for reproducibility (default is True).
-        limit_ratio : flaot, optional
+        rate_dataset : flaot, optional
             Limitation on the number of series for the model (default is 1).
-        split_ratio : flaot, optional
+        training_ratio : flaot, optional
             Limitation on the training series for the model (default is 0.6).
         file_name : str, optional
             Name of the dataset file (default is 'ts').
@@ -688,18 +688,18 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc
         """
         start_time = time.time()  # Record start time
 
-        if limit_ratio < 0.05 or limit_ratio > 1:
+        if rate_dataset < 0.05 or rate_dataset > 1:
             print("\nlimit percentage higher than 100%, reduce to 100% of the dataset")
-            limit_ratio = 1
+            rate_dataset = 1
 
         M = input_data.shape[0]
-        limit = math.ceil(M * limit_ratio)
+        limit = math.ceil(M * rate_dataset)
 
-        if split_ratio < 0.05 or split_ratio > 0.95:
+        if training_ratio < 0.05 or training_ratio > 0.95:
             print("\nsplit ratio to small or to high, reduce to 60% of the dataset")
-            split_ratio = 0.6
+            training_ratio = 0.6
 
-        training_ratio = int(limit * split_ratio)
+        training_ratio = int(limit * training_ratio)
 
         if limit > M:
             limit = M
@@ -718,7 +718,7 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc
         input_data_matrices, obfuscated_matrices = [], []
         output_metrics, output_rmse, input_params, input_params_full = [], [], [], []
 
-        if extractor == "pycatch":
+        if extractor == "pycatch" or extractor == "pycatch22":
             categories, features, _ = Explainer.load_configuration()
 
         for current_series in range(0, limit):
@@ -733,7 +733,7 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc
             input_data_matrices.append(input_data)
             obfuscated_matrices.append(incomp_data)
 
-            if extractor == "pycatch":
+            if extractor == "pycatch" or extractor == "pycatch22":
                 catch_fct, descriptions = Explainer.extractor_pycatch(incomp_data, categories, features, False)
                 extracted_features = np.array(list(catch_fct.values()))
             elif extractor == "tsfel":
diff --git a/imputegap/recovery/imputation.py b/imputegap/recovery/imputation.py
index 75935091..6a3a1a23 100644
--- a/imputegap/recovery/imputation.py
+++ b/imputegap/recovery/imputation.py
@@ -523,11 +523,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data)
-                >>> interpolation_imputer.impute()  # default parameters for imputation > or
-                >>> interpolation_imputer.impute(user_def=True, params={"method":"linear", "poly_order":2})  # user-defined > or
-                >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = interpolation_imputer.recov_data
+                    >>> interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data)
+                    >>> interpolation_imputer.impute()  # default parameters for imputation > or
+                    >>> interpolation_imputer.impute(user_def=True, params={"method":"linear", "poly_order":2})  # user-defined > or
+                    >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = interpolation_imputer.recov_data
                 """
                 if params is not None:
                     method, poly_order = self._check_params(user_def, params)
@@ -574,11 +574,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> knn_imputer = Imputation.Statistics.KNN(incomp_data)
-                >>> knn_imputer.impute()  # default parameters for imputation > or
-                >>> knn_imputer.impute(user_def=True, params={'k': 5, 'weights': "uniform"})  # user-defined > or
-                >>> knn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = knn_imputer.recov_data
+                    >>> knn_imputer = Imputation.Statistics.KNN(incomp_data)
+                    >>> knn_imputer.impute()  # default parameters for imputation > or
+                    >>> knn_imputer.impute(user_def=True, params={'k': 5, 'weights': "uniform"})  # user-defined > or
+                    >>> knn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = knn_imputer.recov_data
                 """
                 if params is not None:
                     k, weights = self._check_params(user_def, params)
@@ -716,11 +716,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data)
-                >>> cdrec_imputer.impute()  # default parameters for imputation > or
-                >>> cdrec_imputer.impute(user_def=True, params={'rank': 5, 'epsilon': 0.01, 'iterations': 100})  # user-defined > or
-                >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
-                >>> recov_data = cdrec_imputer.recov_data
+                    >>> cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data)
+                    >>> cdrec_imputer.impute()  # default parameters for imputation > or
+                    >>> cdrec_imputer.impute(user_def=True, params={'rank': 5, 'epsilon': 0.01, 'iterations': 100})  # user-defined > or
+                    >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> recov_data = cdrec_imputer.recov_data
 
                 References
                 ----------
@@ -774,11 +774,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data)
-                >>> i_svd_imputer.impute()  # default parameters for imputation > or
-                >>> i_svd_imputer.impute(params={'rank': 5}) # user-defined  > or
-                >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = i_svd_imputer.recov_data
+                    >>> i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data)
+                    >>> i_svd_imputer.impute()  # default parameters for imputation > or
+                    >>> i_svd_imputer.impute(params={'rank': 5}) # user-defined  > or
+                    >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = i_svd_imputer.recov_data
 
                 References
                 ----------
@@ -831,11 +831,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data)
-                >>> grouse_imputer.impute()  # default parameters for imputation > or
-                >>> grouse_imputer.impute(params={'max_rank': 5}) # user-defined  > or
-                >>> grouse_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = grouse_imputer.recov_data
+                    >>> grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data)
+                    >>> grouse_imputer.impute()  # default parameters for imputation > or
+                    >>> grouse_imputer.impute(params={'max_rank': 5}) # user-defined  > or
+                    >>> grouse_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = grouse_imputer.recov_data
 
                 References
                 ----------
@@ -891,11 +891,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data)
-                >>> rosl_imputer.impute()  # default parameters for imputation > or
-                >>> rosl_imputer.impute(params={'rank': 5, 'regularization': 10}) # user-defined  > or
-                >>> rosl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = rosl_imputer.recov_data
+                    >>> rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data)
+                    >>> rosl_imputer.impute()  # default parameters for imputation > or
+                    >>> rosl_imputer.impute(params={'rank': 5, 'regularization': 10}) # user-defined  > or
+                    >>> rosl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = rosl_imputer.recov_data
 
                 References
                 ----------
@@ -947,11 +947,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data)
-                >>> soft_impute_imputer.impute()  # default parameters for imputation > or
-                >>> soft_impute_imputer.impute(params={'max_rank': 5}) # user-defined  > or
-                >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = soft_impute_imputer.recov_data
+                    >>> soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data)
+                    >>> soft_impute_imputer.impute()  # default parameters for imputation > or
+                    >>> soft_impute_imputer.impute(params={'max_rank': 5}) # user-defined  > or
+                    >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = soft_impute_imputer.recov_data
 
                 References
                 ----------
@@ -1010,11 +1010,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data)
-                >>> spirit_imputer.impute()  # default parameters for imputation > or
-                >>> spirit_imputer.impute(params={'k': 2, 'w': 5, 'lambda_value': 0.85}) # user-defined  > or
-                >>> spirit_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = spirit_imputer.recov_data
+                    >>> spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data)
+                    >>> spirit_imputer.impute()  # default parameters for imputation > or
+                    >>> spirit_imputer.impute(params={'k': 2, 'w': 5, 'lambda_value': 0.85}) # user-defined  > or
+                    >>> spirit_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = spirit_imputer.recov_data
 
                 References
                 ----------
@@ -1067,11 +1067,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data)
-                >>> svt_imputer.impute()  # default parameters for imputation > or
-                >>> svt_imputer.impute(params={'tau': 1}) # user-defined  > or
-                >>> svt_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = svt_imputer.recov_data
+                    >>> svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data)
+                    >>> svt_imputer.impute()  # default parameters for imputation > or
+                    >>> svt_imputer.impute(params={'tau': 1}) # user-defined  > or
+                    >>> svt_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = svt_imputer.recov_data
 
                 References
                 ----------
@@ -1140,18 +1140,18 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> trmf_imputer = Imputation.MatrixCompletion.SVT(incomp_data)
-                >>> trmf_imputer.impute()  # default parameters for imputation > or
-                >>> trmf_imputer.impute(params={"lags":[], "K":-1, "lambda_f":1.0, "lambda_x":1.0, "lambda_w":1.0, "eta":1.0, "alpha":1000.0, "max_iter":100}) # user-defined > or
-                >>> trmf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = trmf_imputer.recov_data
+                    >>> trmf_imputer = Imputation.MatrixCompletion.TRMF(incomp_data)
+                    >>> trmf_imputer.impute()
+                    >>> trmf_imputer.impute(params={"lags":[], "K":-1, "lambda_f":1.0, "lambda_x":1.0, "lambda_w":1.0, "eta":1.0, "alpha":1000.0, "max_iter":100})
+                    >>> trmf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})
+                    >>> recov_data = trmf_imputer.recov_data
 
                 References
                 ----------
                 H.-F. Yu, N. Rao, and I. S. Dhillon, "Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction," in *Advances in Neural Information Processing Systems*, vol. 29, 2016. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2016/file/85422afb467e9456013a2a51d4dff702-Paper.pdf
                 """
                 if params is not None:
-                    lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_iter = self._check_params(user_def, params)[0]
+                    lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_iter = self._check_params(user_def, params)
                 else:
                     lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_iter = utils.load_parameters(query="default", algorithm=self.algorithm)
 
@@ -1226,11 +1226,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> mf_imputer = Imputation.MachineLearning.MissForest(incomp_data)
-                >>> mf_imputer.impute()  # default parameters for imputation > or
-                >>> mf_imputer.impute(user_def=True, params={"n_estimators":10, "max_iter":3, "max_features":"sqrt", "seed": 42})  # user defined > or
-                >>> mf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = mf_imputer.recov_data
+                    >>> mf_imputer = Imputation.MachineLearning.MissForest(incomp_data)
+                    >>> mf_imputer.impute()  # default parameters for imputation > or
+                    >>> mf_imputer.impute(user_def=True, params={"n_estimators":10, "max_iter":3, "max_features":"sqrt", "seed": 42})  # user defined > or
+                    >>> mf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = mf_imputer.recov_data
 
                 References
                 ----------
@@ -1255,7 +1255,7 @@ class MICE(BaseImputer):
             Methods
             -------
             impute(self, user_def=True, params=None):
-                Perform imputation using the STMVL algorithm.
+                Perform imputation using the MICE algorithm.
             """
             algorithm = "mice"
 
@@ -1266,33 +1266,33 @@ def impute(self, user_def=True, params=None):
                 Parameters
                 ----------
                 user_def : bool, optional
-                    Whether to use user-defined or default parameters (default is True).
+                    Whether to use user-defined or default parameters (default is True). \n
                 params : dict, optional
-                    Parameters of the STMVL algorithm, if None, default ones are loaded.
+                    Parameters of the MICE algorithm, if None, default ones are loaded. \n
 
                     **Algorithm parameters:**
 
                     max_iter : int, optional
-                        Maximum number of imputation rounds to perform before returning the imputations computed during the final round. (default is 3).
+                        Maximum number of imputation rounds to perform before returning the imputations computed during the final round. (default is 3). \n
                     tol : float, optional
-                        Tolerance of the stopping condition. (default is 0.001).
+                        Tolerance of the stopping condition. (default is 0.001). \n
                     initial_strategy : str, optional
-                        Which strategy to use to initialize the missing values. {‘mean’, ‘median’, ‘most_frequent’, ‘constant’} (default is "means").
+                        Which strategy to use to initialize the missing values. {‘mean’, ‘median’, ‘most_frequent’, ‘constant’} (default is "means"). \n
                     seed : int, optional
-                        The seed of the pseudo random number generator to use. Randomizes selection of estimator features (default is 42).
+                        The seed of the pseudo random number generator to use. Randomizes selection of estimator features (default is 42). \n
 
                 Returns
                 -------
-                self : MICE
-                    The object with `recov_data` set.
+                    self : MICE
+                        The object with `recov_data` set.
 
                 Example
                 -------
-                >>> mice_imputer = Imputation.MachineLearning.MICE(incomp_data)
-                >>> mice_imputer.impute()  # default parameters for imputation > or
-                >>> mice_imputer.impute(user_def=True, params={"max_iter":3, "tol":0.001, "initial_strategy":"mean", "seed": 42})  # user defined > or
-                >>> mice_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = mice_imputer.recov_data
+                    >>> mice_imputer = Imputation.MachineLearning.MICE(incomp_data)
+                    >>> mice_imputer.impute()  # default parameters for imputation > or
+                    >>> mice_imputer.impute(user_def=True, params={"max_iter":3, "tol":0.001, "initial_strategy":"mean", "seed": 42})  # user defined > or
+                    >>> mice_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = mice_imputer.recov_data
 
                 References
                 ----------
@@ -1346,11 +1346,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> mxgboost_imputer = Imputation.MachineLearning.MICE(incomp_data)
-                >>> mxgboost_imputer.impute()  # default parameters for imputation > or
-                >>> mxgboost_imputer.impute(user_def=True, params={"n_estimators":3, "seed": 42})  # user defined > or
-                >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = mxgboost_imputer.recov_data
+                    >>> mxgboost_imputer = Imputation.MachineLearning.XGBOOST(incomp_data)
+                    >>> mxgboost_imputer.impute()  # default parameters for imputation > or
+                    >>> mxgboost_imputer.impute(user_def=True, params={"n_estimators":3, "seed": 42})  # user defined > or
+                    >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = mxgboost_imputer.recov_data
 
                 References
                 ----------
@@ -1401,11 +1401,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> iim_imputer = Imputation.MachineLearning.IIM(incomp_data)
-                >>> iim_imputer.impute()  # default parameters for imputation > or
-                >>> iim_imputer.impute(user_def=True, params={'learning_neighbors': 10})  # user-defined  > or
-                >>> iim_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
-                >>> recov_data = iim_imputer.recov_data
+                    >>> iim_imputer = Imputation.MachineLearning.IIM(incomp_data)
+                    >>> iim_imputer.impute()  # default parameters for imputation > or
+                    >>> iim_imputer.impute(user_def=True, params={'learning_neighbors': 10})  # user-defined  > or
+                    >>> iim_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> recov_data = iim_imputer.recov_data
 
                 References
                 ----------
@@ -1478,11 +1478,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data)
-                >>> stmvl_imputer.impute()  # default parameters for imputation > or
-                >>> stmvl_imputer.impute(user_def=True, params={'window_size': 7, 'learning_rate':0.01, 'gamma':0.85, 'alpha': 7})  # user-defined  > or
-                >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
-                >>> recov_data = stmvl_imputer.recov_data
+                    >>> stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data)
+                    >>> stmvl_imputer.impute()  # default parameters for imputation > or
+                    >>> stmvl_imputer.impute(user_def=True, params={'window_size': 7, 'learning_rate':0.01, 'gamma':0.85, 'alpha': 7})  # user-defined  > or
+                    >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> recov_data = stmvl_imputer.recov_data
 
                 References
                 ----------
@@ -1538,11 +1538,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data)
-                >>> dynammo_imputer.impute()  # default parameters for imputation > or
-                >>> dynammo_imputer.impute(params={'h': 5, 'max_iteration': 100, 'approximation': True}) # user-defined  > or
-                >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = dynammo_imputer.recov_data
+                    >>> dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data)
+                    >>> dynammo_imputer.impute()  # default parameters for imputation > or
+                    >>> dynammo_imputer.impute(params={'h': 5, 'max_iteration': 100, 'approximation': True}) # user-defined  > or
+                    >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = dynammo_imputer.recov_data
 
                 References
                 ----------
@@ -1593,11 +1593,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data)
-                >>> tkcm_imputer.impute()  # default parameters for imputation > or
-                >>> tkcm_imputer.impute(params={'rank': 5})  # user-defined > or
-                >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = tkcm_imputer.recov_data
+                    >>> tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data)
+                    >>> tkcm_imputer.impute()  # default parameters for imputation > or
+                    >>> tkcm_imputer.impute(params={'rank': 5})  # user-defined > or
+                    >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = tkcm_imputer.recov_data
 
                 References
                 ----------
@@ -1680,11 +1680,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data)
-                >>> mrnn_imputer.impute()  # default parameters for imputation > or
-                >>> mrnn_imputer.impute(user_def=True, params={'hidden_dim': 10, 'learning_rate':0.01, 'iterations':50, 'sequence_length': 7})  # user-defined > or
-                >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
-                >>> recov_data = mrnn_imputer.recov_data
+                    >>> mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data)
+                    >>> mrnn_imputer.impute()  # default parameters for imputation > or
+                    >>> mrnn_imputer.impute(user_def=True, params={'hidden_dim': 10, 'learning_rate':0.01, 'iterations':50, 'sequence_length': 7})  # user-defined > or
+                    >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> recov_data = mrnn_imputer.recov_data
 
                 References
                 ----------
@@ -1743,11 +1743,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> brits_imputer = Imputation.DeepLearning.BRITS(incomp_data)
-                >>> brits_imputer.impute()  # default parameters for imputation > or
-                >>> brits_imputer.impute(params={"model": "brits", "epoch": 2, "batch_size": 10, "nbr_features": 1, "hidden_layer": 64})  # user-defined > or
-                >>> brits_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = brits_imputer.recov_data
+                    >>> brits_imputer = Imputation.DeepLearning.BRITS(incomp_data)
+                    >>> brits_imputer.impute()  # default parameters for imputation > or
+                    >>> brits_imputer.impute(params={"model": "brits", "epoch": 2, "batch_size": 10, "nbr_features": 1, "hidden_layer": 64})  # user-defined > or
+                    >>> brits_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = brits_imputer.recov_data
 
                 References
                 ----------
@@ -1801,11 +1801,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data)
-                >>> deep_mvi_imputer.impute()  # default parameters for imputation > or
-                >>> deep_mvi_imputer.impute(params={"max_epoch": 10, "patience": 2})  # user-defined > or
-                >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = deep_mvi_imputer.recov_data
+                    >>> deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data)
+                    >>> deep_mvi_imputer.impute()  # default parameters for imputation > or
+                    >>> deep_mvi_imputer.impute(params={"max_epoch": 10, "patience": 2})  # user-defined > or
+                    >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = deep_mvi_imputer.recov_data
 
                 References
                 ----------
@@ -1871,11 +1871,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data)
-                >>> mpin_imputer.impute()  # default parameters for imputation > or
-                >>> mpin_imputer.impute(params={"incre_mode": "data+state", "window": 1, "k": 15, "learning_rate": 0.001, "weight_decay": 0.2, "epochs": 6, "num_of_iteration": 6, "threshold": 0.50, "base": "GCN"})  # user-defined > or
-                >>> mpin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = mpin_imputer.recov_data
+                    >>> mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data)
+                    >>> mpin_imputer.impute()  # default parameters for imputation > or
+                    >>> mpin_imputer.impute(params={"incre_mode": "data+state", "window": 1, "k": 15, "learning_rate": 0.001, "weight_decay": 0.2, "epochs": 6, "num_of_iteration": 6, "threshold": 0.50, "base": "GCN"})  # user-defined > or
+                    >>> mpin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = mpin_imputer.recov_data
 
                 References
                 ----------
@@ -1932,11 +1932,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data)
-                >>> pristi_imputer.impute()  # default parameters for imputation > or
-                >>> pristi_imputer.impute(params={"target_strategy":"hybrid", "unconditional":True, "seed":42, "device":"cpu"})  # user-defined > or
-                >>> pristi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
-                >>> recov_data = pristi_imputer.recov_data
+                    >>> pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data)
+                    >>> pristi_imputer.impute()  # default parameters for imputation > or
+                    >>> pristi_imputer.impute(params={"target_strategy":"hybrid", "unconditional":True, "seed":42, "device":"cpu"})  # user-defined > or
+                    >>> pristi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> recov_data = pristi_imputer.recov_data
 
                 References
                 ----------
@@ -1999,11 +1999,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data)
-                >>> miss_net_imputer.impute()  # default parameters for imputation > or
-                >>> miss_net_imputer.impute(user_def=True, params={'alpha': 0.5, 'beta':0.1, 'L':10, 'n_cl': 1, 'max_iteration':20, 'tol':5, 'random_init':False})  # user-defined > or
-                >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
-                >>> recov_data = miss_net_imputer.recov_data
+                    >>> miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data)
+                    >>> miss_net_imputer.impute()  # default parameters for imputation > or
+                    >>> miss_net_imputer.impute(user_def=True, params={'alpha': 0.5, 'beta':0.1, 'L':10, 'n_cl': 1, 'max_iteration':20, 'tol':5, 'random_init':False})  # user-defined > or
+                    >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> recov_data = miss_net_imputer.recov_data
 
                 References
                 ----------
@@ -2066,11 +2066,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> gain_imputer = Imputation.DeepLearning.GAIN(incomp_data)
-                >>> gain_imputer.impute()  # default parameters for imputation > or
-                >>> gain_imputer.impute(user_def=True, params={"batch_size":32, "hint_rate":0.9, "alpha":10, "epoch":100})  # user defined> or
-                >>> gain_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
-                >>> recov_data = gain_imputer.recov_data
+                    >>> gain_imputer = Imputation.DeepLearning.GAIN(incomp_data)
+                    >>> gain_imputer.impute()  # default parameters for imputation > or
+                    >>> gain_imputer.impute(user_def=True, params={"batch_size":32, "hint_rate":0.9, "alpha":10, "epoch":100})  # user defined> or
+                    >>> gain_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> recov_data = gain_imputer.recov_data
 
                 References
                 ----------
@@ -2145,11 +2145,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> grin_imputer = Imputation.DeepLearning.GRIN(incomp_data)
-                >>> grin_imputer.impute()  # default parameters for imputation > or
-                >>> grin_imputer.impute(user_def=True, params={"d_hidden":32, "lr":0.001, "batch_size":32, "window":1, "alpha":10.0, "patience":4, "epochs":20, "workers":2})  # user defined> or
-                >>> grin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
-                >>> recov_data = grin_imputer.recov_data
+                    >>> grin_imputer = Imputation.DeepLearning.GRIN(incomp_data)
+                    >>> grin_imputer.impute()  # default parameters for imputation > or
+                    >>> grin_imputer.impute(user_def=True, params={"d_hidden":32, "lr":0.001, "batch_size":32, "window":1, "alpha":10.0, "patience":4, "epochs":20, "workers":2})  # user defined> or
+                    >>> grin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> recov_data = grin_imputer.recov_data
 
                 References
                 ----------
@@ -2230,11 +2230,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data)
-                >>> bay_otide_imputer.impute()  # default parameters for imputation > or
-                >>> bay_otide_imputer.impute(user_def=True, params={"K_trend":20, "K_season":2, "n_season":5, "K_bias":1, "time_scale":1, "a0":0.6, "b0":2.5, "v":0.5})  # user defined> or
-                >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
-                >>> recov_data = bay_otide_imputer.recov_data
+                    >>> bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data)
+                    >>> bay_otide_imputer.impute()  # default parameters for imputation > or
+                    >>> bay_otide_imputer.impute(user_def=True, params={"K_trend":20, "K_season":2, "n_season":5, "K_bias":1, "time_scale":1, "a0":0.6, "b0":2.5, "v":0.5})  # user defined> or
+                    >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> recov_data = bay_otide_imputer.recov_data
 
                 References
                 ----------
@@ -2296,11 +2296,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data)
-                >>> hkmf_t_imputer.impute()  # default parameters for imputation > or
-                >>> hkmf_t_imputer.impute(user_def=True, params={"tags":None, "data_names":None, "epoch":5})  # user defined> or
-                >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
-                >>> recov_data = hkmf_t_imputer.recov_data
+                    >>> hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data)
+                    >>> hkmf_t_imputer.impute()  # default parameters for imputation > or
+                    >>> hkmf_t_imputer.impute(user_def=True, params={"tags":None, "data_names":None, "epoch":5})  # user defined> or
+                    >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> recov_data = hkmf_t_imputer.recov_data
 
                 References
                 ----------
@@ -2383,11 +2383,11 @@ def impute(self, user_def=True, params=None):
 
                 Example
                 -------
-                >>> bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data)
-                >>> bit_graph_imputer.impute()  # default parameters for imputation > or
-                >>> bit_graph_imputer.impute(user_def=True, params={"node_number":-1, "kernel_set":[1], "dropout":0.1, "subgraph_size":5, "node_dim":3, "seq_len":1, "lr":0.001, "epoch":10, "seed":42})  # user defined> or
-                >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
-                >>> recov_data = bit_graph_imputer.recov_data
+                    >>> bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data)
+                    >>> bit_graph_imputer.impute()  # default parameters for imputation > or
+                    >>> bit_graph_imputer.impute(user_def=True, params={"node_number":-1, "kernel_set":[1], "dropout":0.1, "subgraph_size":5, "node_dim":3, "seq_len":1, "lr":0.001, "epoch":10, "seed":42})  # user defined> or
+                    >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> recov_data = bit_graph_imputer.recov_data
 
                 References
                 ----------
diff --git a/imputegap/recovery/manager.py b/imputegap/recovery/manager.py
index fbc50953..de9ace68 100644
--- a/imputegap/recovery/manager.py
+++ b/imputegap/recovery/manager.py
@@ -314,7 +314,7 @@ def normalize(self, normalizer="z_score"):
         print(f"\n\t\t> logs, normalization {normalizer} - Execution Time: {(end_time - start_time):.4f} seconds\n")
 
     def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, nbr_val=None, series_range=None,
-             subplot=False, size=(16, 8), save_path="./imputegap/assets", display=True):
+             subplot=False, size=(16, 8), save_path="./imputegap_assets", display=True):
         """
         Plot the time series data, including raw, contaminated, or imputed data.
 
@@ -435,7 +435,7 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n
                     ax.set_title('Series ' + str(i+1), fontsize=9)
                     ax.set_xlabel('Timestamp', fontsize=7)
                     ax.set_ylabel('Values', fontsize=7)
-                    ax.legend(loc='upper left', fontsize=7)
+                    ax.legend(loc='upper left', fontsize=6, frameon=True, fancybox=True, framealpha=0.8)
                     plt.tight_layout()
 
                 number_of_series += 1
diff --git a/imputegap/report.log b/imputegap/report.log
index 0606aa98..e69de29b 100644
--- a/imputegap/report.log
+++ b/imputegap/report.log
@@ -1,113 +0,0 @@
-2025-03-06 18:22:32,474 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:22:32,481 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
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-2025-03-06 18:35:50,581 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
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-2025-03-06 18:36:04,594 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
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-2025-03-06 18:36:46,120 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:36:46,133 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:36:47,513 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
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-2025-03-06 18:40:51,117 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:40:51,123 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
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-2025-03-06 18:41:40,100 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
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-2025-03-06 18:41:40,996 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:41:52,207 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:41:52,211 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:41:53,818 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
-2025-03-06 18:41:53,838 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor.
diff --git a/imputegap/runner_contamination.py b/imputegap/runner_contamination.py
index 3862d996..d1560e8d 100644
--- a/imputegap/runner_contamination.py
+++ b/imputegap/runner_contamination.py
@@ -13,4 +13,4 @@
 ts_m = ts.Contamination.missing_completely_at_random(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True)
 
 # plot the contaminated time series
-ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap/assets")
\ No newline at end of file
+ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets")
\ No newline at end of file
diff --git a/imputegap/runner_datasets.py b/imputegap/runner_datasets.py
deleted file mode 100644
index 1662d1d8..00000000
--- a/imputegap/runner_datasets.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from imputegap.recovery.explainer import Explainer
-from imputegap.recovery.manager import TimeSeries
-from imputegap.tools import utils
-
-datasets = ["electricity", "soccer", "temperature", "motion"]
-
-for dataset in datasets:
-    # small one
-    data_n = TimeSeries()
-    data_n.load_series(data=utils.search_path(dataset), nbr_series=20, nbr_val=400, header=False)
-    data_n.plot(input_data=data_n.data, nbr_series=20, save_path="./dataset/docs/" + dataset + "", display=False)
-    data_n.plot(input_data=data_n.data, nbr_series=1, save_path="./dataset/docs/" + dataset + "", display=False)
-    data_n.normalize(normalizer="min_max")
-    data_n.plot(input_data=data_n.data, nbr_series=20, save_path="./dataset/docs/" + dataset + "", display=False)
-
-    # 5x one
-    data_n = TimeSeries()
-    max_series = 3
-    max_value = 500
-    if dataset == "bafu":
-        max_value = 10000
-    elif dataset == "chlorine":
-        max_value = 1000
-    elif dataset == "eeg-alcohol":
-        max_value = 256
-    elif dataset == "eeg-reading":
-        max_value = 1201
-    elif dataset == "drift":
-        max_value = 400
-
-    data_n.load_series(data=utils.search_path(dataset), nbr_series=max_series, nbr_val=max_value, header=False)
-    data_n.plot(input_data=data_n.data, save_path="./dataset/docs/" + dataset + "", display=False)
-    data_n.normalize(normalizer="min_max")
-    data_n.plot(input_data=data_n.data, save_path="./dataset/docs/" + dataset + "", display=False)
-
-    # full one
-    data_n = TimeSeries()
-    data_n.load_series(data=utils.search_path(dataset), header=False)
-    data_n.plot(input_data=data_n.data, save_path="./dataset/docs/" + dataset + "", display=False)
-
-    categories, features, _ = Explainer.load_configuration()
-    characteristics, descriptions = Explainer.extractor_pycatch(data=data_n.data, features_categories=categories, features_list=features, do_catch24=False)
-
-    p = "./dataset/docs/"+dataset+"/features_"+dataset+".txt"
-    with open(p, 'w') as f:
-        for desc in descriptions:
-            key, category, description = desc
-            if key in characteristics:
-                value = characteristics[key]
-                f.write(f"|{category}|{description}|{value}|\n")
-            else:
-                f.write(f"Warning: Key '{key}' not found in characteristics!\n")
-    print(f"Table exported to {p}")
diff --git a/imputegap/runner_downstream.py b/imputegap/runner_downstream.py
index 5c152944..84376518 100644
--- a/imputegap/runner_downstream.py
+++ b/imputegap/runner_downstream.py
@@ -7,7 +7,7 @@
 print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}")
 
 # load and normalize the timeseries
-ts.load_series(utils.search_path("chlorine"))
+ts.load_series(utils.search_path("forecast-economy"))
 ts.normalize(normalizer="min_max")
 
 # contaminate the time series
@@ -18,6 +18,6 @@
 imputer.impute()
 
 # compute print the downstream results
-downstream_config = {"task": "forecast", "model": "prophet"}
+downstream_config = {"task": "forecast", "model": "hw-add"}
 imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
 ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)
\ No newline at end of file
diff --git a/imputegap/runner_features.py b/imputegap/runner_features.py
new file mode 100644
index 00000000..f80f750a
--- /dev/null
+++ b/imputegap/runner_features.py
@@ -0,0 +1,22 @@
+from imputegap.recovery.explainer import Explainer
+from imputegap.recovery.manager import TimeSeries
+from imputegap.tools import utils
+
+dataset = "temperature"
+
+ts = TimeSeries()
+ts.load_series(data=utils.search_path(dataset), header=False)
+
+categories, features, _ = Explainer.load_configuration()
+characteristics, descriptions = Explainer.extractor_pycatch(data=ts.data, features_categories=categories, features_list=features, do_catch24=False)
+
+p = "./dataset/docs/"+dataset+"/features_"+dataset+".txt"
+with open(p, 'w') as f:
+    for desc in descriptions:
+        key, category, description = desc
+        if key in characteristics:
+            value = characteristics[key]
+            f.write(f"|{category}|{description}|{value}|\n")
+        else:
+            f.write(f"Warning: Key '{key}' not found in characteristics!\n")
+print(f"Table exported to {p}")
diff --git a/imputegap/runner_imputation.py b/imputegap/runner_imputation.py
index beaec3f4..546920be 100644
--- a/imputegap/runner_imputation.py
+++ b/imputegap/runner_imputation.py
@@ -22,4 +22,4 @@
 ts.print_results(imputer.metrics)
 
 # plot the recovered time series
-ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets")
\ No newline at end of file
+ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets")
\ No newline at end of file
diff --git a/imputegap/runner_loading.py b/imputegap/runner_loading.py
index 32a195b8..9b1d89a4 100644
--- a/imputegap/runner_loading.py
+++ b/imputegap/runner_loading.py
@@ -7,10 +7,10 @@
 
 
 # load the timeseries from file or from the code
-ts.load_series(utils.search_path("eeg-alcohol"), nbr_series=1)
+ts.load_series(utils.search_path("eeg-alcohol"))
 
 # plot a subset of time series
-ts.plot(input_data=ts.data, save_path="./imputegap/assets")
+ts.plot(input_data=ts.data, save_path="./imputegap_assets")
 
 # print a subset of time series
 ts.print(nbr_series=6, nbr_val=20)
diff --git a/imputegap/runner_optimization.py b/imputegap/runner_optimization.py
index 52eabe69..e609dc75 100644
--- a/imputegap/runner_optimization.py
+++ b/imputegap/runner_optimization.py
@@ -22,7 +22,7 @@
 ts.print_results(imputer.metrics)
 
 # plot the recovered time series
-ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap/assets", display=True)
+ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True)
 
 # save hyperparameters
 utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune")
\ No newline at end of file
diff --git a/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc b/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc
index 52e5fec7..b924e14f 100644
Binary files a/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc and b/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc differ
diff --git a/imputegap/tools/__pycache__/utils.cpython-312.pyc b/imputegap/tools/__pycache__/utils.cpython-312.pyc
index e2b08b1d..31f44198 100644
Binary files a/imputegap/tools/__pycache__/utils.cpython-312.pyc and b/imputegap/tools/__pycache__/utils.cpython-312.pyc differ
diff --git a/imputegap/tools/algorithm_parameters.py b/imputegap/tools/algorithm_parameters.py
index ab752eef..5d8f3b1a 100644
--- a/imputegap/tools/algorithm_parameters.py
+++ b/imputegap/tools/algorithm_parameters.py
@@ -74,9 +74,9 @@
         #"keep_prob": tune.loguniform(1e-6, 1)  # Dropout keep probability
     },
     "stmvl": {
-        "window_size": tune.grid_search([i for i in range(2, 100)]),  # Window size
+        "window_size": tune.grid_search([i for i in range(10, 100)]),  # Window size
         "gamma": tune.loguniform(1e-6, 1),  # Smoothing parameter gamma
-        "alpha": tune.grid_search([i for i in range(1, 10)])  # Smoothing parameter alpha
+        "alpha": tune.grid_search([i for i in range(2, 10)]),  # Window size
     },
 
     # --- New Algorithms ---
diff --git a/imputegap/tools/utils.py b/imputegap/tools/utils.py
index e0b37f14..a52bd7ce 100644
--- a/imputegap/tools/utils.py
+++ b/imputegap/tools/utils.py
@@ -138,6 +138,82 @@ def config_contamination(ts, pattern, dataset_rate=0.4, series_rate=0.4, block_s
     return incomp_data
 
 
+def config_forecaster(model, params):
+        """
+        Configure and execute forecaster model for downstream analytics
+
+        Parameters
+        ----------
+        model : str
+            name of the forcaster model
+        params : list of params
+            List of paramaters for a forcaster model
+
+        Returns
+        -------
+        Forecaster object (SKTIME/DART)
+            Forecaster object for downstream analytics
+        """
+
+        if model == "prophet":
+            from sktime.forecasting.fbprophet import Prophet
+            forecaster = Prophet(**params)
+        elif model == "exp-smoothing":
+            from sktime.forecasting.exp_smoothing import ExponentialSmoothing
+            forecaster = ExponentialSmoothing(**params)
+        elif model == "nbeats":
+            from darts.models import NBEATSModel
+            forecaster = NBEATSModel(**params)
+        elif model == "xgboost":
+            from darts.models.forecasting.xgboost import XGBModel
+            forecaster = XGBModel(**params)
+        elif model == "lightgbm":
+            from darts.models.forecasting.lgbm import LightGBMModel
+            forecaster = LightGBMModel(**params)
+        elif model == "lstm":
+            from darts.models.forecasting.rnn_model import RNNModel
+            forecaster = RNNModel(**params)
+        elif model == "deepar":
+            from darts.models.forecasting.rnn_model import RNNModel
+            forecaster = RNNModel(**params)
+        elif model == "transformer":
+            from darts.models.forecasting.transformer_model import TransformerModel
+            forecaster = TransformerModel(**params)
+        elif model == "hw-add":
+            from sktime.forecasting.exp_smoothing import ExponentialSmoothing
+            forecaster = ExponentialSmoothing(**params)
+        elif model == "arima":
+            from sktime.forecasting.arima import AutoARIMA
+            forecaster = AutoARIMA(**params)
+        elif model == "sf-arima":
+            from sktime.forecasting.statsforecast import StatsForecastAutoARIMA
+            forecaster = StatsForecastAutoARIMA(**params)
+            forecaster.set_config(warnings='off')
+        elif model == "bats":
+            from sktime.forecasting.bats import BATS
+            forecaster = BATS(**params)
+        elif model == "ets":
+            from sktime.forecasting.ets import AutoETS
+            forecaster = AutoETS(**params)
+        elif model == "croston":
+            from sktime.forecasting.croston import Croston
+            forecaster = Croston(**params)
+        elif model == "theta":
+            from sktime.forecasting.theta import ThetaForecaster
+            forecaster = ThetaForecaster(**params)
+        elif model == "unobs":
+            from sktime.forecasting.structural import UnobservedComponents
+            forecaster = UnobservedComponents(**params)
+
+
+        else:
+            from sktime.forecasting.naive import NaiveForecaster
+            forecaster = NaiveForecaster(**params)
+
+        return forecaster
+
+
+
 def __marshal_as_numpy_column(__ctype_container, __py_sizen, __py_sizem):
     """
     Marshal a ctypes container as a numpy column-major array.
@@ -503,6 +579,117 @@ def load_parameters(query: str = "default", algorithm: str = "cdrec", dataset: s
         seasonality_mode = str(config[algorithm]['seasonality_mode'])
         n_changepoints = int(config[algorithm]['n_changepoints'])
         return {"seasonality_mode": seasonality_mode, "n_changepoints": n_changepoints}
+    elif algorithm == "forecaster-nbeats":
+        input_chunk_length = int(config[algorithm]['input_chunk_length'])
+        output_chunk_length = int(config[algorithm]['output_chunk_length'])
+        num_blocks = int(config[algorithm]['num_blocks'])
+        layer_widths = int(config[algorithm]['layer_widths'])
+        random_state = int(config[algorithm]['random_state'])
+        n_epochs = int(config[algorithm]['n_epochs'])
+        pl_trainer_kwargs = str(config[algorithm]['pl_trainer_kwargs'])
+        if pl_trainer_kwargs == "cpu":
+            drive = {"accelerator": pl_trainer_kwargs}
+        else:
+            drive = {"accelerator": pl_trainer_kwargs, "devices": [0]}
+        return {"input_chunk_length": input_chunk_length, "output_chunk_length": output_chunk_length, "num_blocks": num_blocks,
+                "layer_widths": layer_widths, "random_state": random_state, "n_epochs": n_epochs, "pl_trainer_kwargs": drive}
+    elif algorithm == "forecaster-xgboost":
+        lags = int(config[algorithm]['lags'])
+        return {"lags": lags}
+    elif algorithm == "forecaster-lightgbm":
+        lags = int(config[algorithm]['lags'])
+        verbose = int(config[algorithm]['verbose'])
+        return {"lags": lags, "verbose": verbose}
+    elif algorithm == "forecaster-lstm":
+        input_chunk_length = int(config[algorithm]['input_chunk_length'])
+        model = str(config[algorithm]['model'])
+        random_state = int(config[algorithm]['random_state'])
+        n_epochs = int(config[algorithm]['n_epochs'])
+        pl_trainer_kwargs = str(config[algorithm]['pl_trainer_kwargs'])
+        if pl_trainer_kwargs == "cpu":
+            drive = {"accelerator": pl_trainer_kwargs}
+        else:
+            drive = {"accelerator": pl_trainer_kwargs, "devices": [0]}
+        return {"input_chunk_length": input_chunk_length, "model": model, "random_state": random_state, "n_epochs": n_epochs, "pl_trainer_kwargs": drive}
+    elif algorithm == "forecaster-deepar":
+        input_chunk_length = int(config[algorithm]['input_chunk_length'])
+        model = str(config[algorithm]['model'])
+        random_state = int(config[algorithm]['random_state'])
+        n_epochs = int(config[algorithm]['n_epochs'])
+        pl_trainer_kwargs = str(config[algorithm]['pl_trainer_kwargs'])
+        if pl_trainer_kwargs == "cpu":
+            drive = {"accelerator": pl_trainer_kwargs}
+        else:
+            drive = {"accelerator": pl_trainer_kwargs, "devices": [0]}
+        return {"input_chunk_length": input_chunk_length, "model": model, "random_state": random_state, "n_epochs": n_epochs, "pl_trainer_kwargs": drive}
+    elif algorithm == "forecaster-transformer":
+        input_chunk_length = int(config[algorithm]['input_chunk_length'])
+        output_chunk_length = int(config[algorithm]['output_chunk_length'])
+        random_state = int(config[algorithm]['random_state'])
+        n_epochs = int(config[algorithm]['n_epochs'])
+        pl_trainer_kwargs = str(config[algorithm]['pl_trainer_kwargs'])
+        if pl_trainer_kwargs == "cpu":
+            drive = {"accelerator": pl_trainer_kwargs}
+        else:
+            drive = {"accelerator": pl_trainer_kwargs, "devices": [0]}
+        return {"input_chunk_length": input_chunk_length, "output_chunk_length": output_chunk_length, "random_state": random_state, "n_epochs": n_epochs, "pl_trainer_kwargs": drive}
+
+    elif algorithm == "forecaster-hw-add":
+        sp = int(config[algorithm]['sp'])
+        trend = str(config[algorithm]['trend'])
+        seasonal = str(config[algorithm]['seasonal'])
+        return {"sp": sp, "trend": trend, "seasonal": seasonal}
+    elif algorithm == "forecaster-arima":
+        sp = int(config[algorithm]['sp'])
+        suppress_warnings = bool(config[algorithm]['suppress_warnings'])
+        start_p = int(config[algorithm]['start_p'])
+        start_q = int(config[algorithm]['start_q'])
+        max_p = int(config[algorithm]['max_p'])
+        max_q = int(config[algorithm]['max_q'])
+        start_P = int(config[algorithm]['start_P'])
+        seasonal = int(config[algorithm]['seasonal'])
+        d = int(config[algorithm]['d'])
+        D = int(config[algorithm]['D'])
+        return {"sp": sp, "suppress_warnings": suppress_warnings, "start_p": start_p, "start_q": start_q,
+                "max_p": max_p, "max_q": max_q, "start_P": start_P, "seasonal": seasonal, "d": d, "D": D}
+    elif algorithm == "forecaster-sf-arima":
+        sp = int(config[algorithm]['sp'])
+        start_p = int(config[algorithm]['start_p'])
+        start_q = int(config[algorithm]['start_q'])
+        max_p = int(config[algorithm]['max_p'])
+        max_q = int(config[algorithm]['max_q'])
+        start_P = int(config[algorithm]['start_P'])
+        seasonal = int(config[algorithm]['seasonal'])
+        d = int(config[algorithm]['d'])
+        D = int(config[algorithm]['D'])
+        return {"sp": sp, "start_p": start_p, "start_q": start_q,
+                "max_p": max_p, "max_q": max_q, "start_P": start_P, "seasonal": seasonal, "d": d, "D": D}
+    elif algorithm == "forecaster-bats":
+        sp = int(config[algorithm]['sp'])
+        use_trend = bool(config[algorithm]['use_trend'])
+        use_box_cox = bool(config[algorithm]['use_box_cox'])
+        return {"sp": sp, "use_trend": use_trend, "use_box_cox": use_box_cox}
+    elif algorithm == "forecaster-ets":
+        sp = int(config[algorithm]['sp'])
+        auto = bool(config[algorithm]['auto'])
+        return {"sp": sp, "auto": auto}
+    elif algorithm == "forecaster-croston":
+        smoothing = float(config[algorithm]['smoothing'])
+        return {"smoothing": smoothing}
+    elif algorithm == "forecaster-unobs":
+        level = bool(config[algorithm]['level'])
+        trend = bool(config[algorithm]['trend'])
+        sp = int(config[algorithm]['sp'])
+        return {"level": level, "trend": trend, "seasonal": sp}
+    elif algorithm == "forecaster-theta":
+        sp = int(config[algorithm]['sp'])
+        deseasonalize = bool(config[algorithm]['deseasonalize'])
+        return {"sp": sp, "deseasonalize": deseasonalize}
+    elif algorithm == "forecaster-rnn":
+        input_size = int(config[algorithm]['input_size'])
+        inference_input_size = int(config[algorithm]['inference_input_size'])
+        return {"input_size": input_size, "inference_input_size": inference_input_size}
+
     elif algorithm == "colors":
         colors = config[algorithm]['plot']
         return colors
@@ -869,7 +1056,8 @@ def list_of_datasets(txt=False):
         "electricity",
         "motion",
         "soccer",
-        "temperature"
+        "temperature",
+        "forecast-economy"
     ])
 
     if txt:
@@ -887,9 +1075,30 @@ def list_of_optimizers():
     ])
 
 def list_of_downstreams():
+    return sorted(list_of_downstreams_sktime() + list_of_downstreams_darts())
+
+
+def list_of_downstreams_sktime():
     return sorted([
         "prophet",
         "exp-smoothing",
+        "hw-add",
+        "arima",
+        "sf-arima",
+        "bats",
+        "ets",
+        "croston",
+        "theta",
+        "unobs",
         "naive"
     ])
 
+def list_of_downstreams_darts():
+    return sorted([
+        "nbeats",
+        "xgboost",
+        "lightgbm",
+        "lstm",
+        "deepar",
+        "transformer"
+    ])
diff --git a/requirements.txt b/requirements.txt
index 5320dda0..cec68d2b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -24,11 +24,15 @@ sktime==0.35.0
 statsmodels==0.14.4
 prophet==1.1.6
 plotly==5.24.1
+darts<0.34.0
+
 
 # PyTorch and Related Libraries
 torch==2.5.1
 torchvision==0.20.1
 torchaudio==2.5.1
+# neuralforecast==1.6.4
+
 
 # PyTorch Geometric and Extensions (use prebuilt binaries)
 #-f https://data.pyg.org/whl/torch-2.5.1.html
diff --git a/tests/test_downstream.py b/tests/test_downstream.py
index b2876ddf..f0948aeb 100644
--- a/tests/test_downstream.py
+++ b/tests/test_downstream.py
@@ -13,7 +13,7 @@ def test_downstream(self):
         """
         # Load and normalize the series
         ts_1 = TimeSeries()
-        ts_1.load_series(utils.search_path("chlorine"))
+        ts_1.load_series(utils.search_path("forecast-economy"))
         ts_1.normalize(normalizer="min_max")
 
         # Create a mask for contamination
@@ -26,7 +26,8 @@ def test_downstream(self):
         # Configure downstream options
         downstream_options = [{"task": "forecast", "model": "prophet", "params": None, "plots": False},
                               {"task": "forecast", "model": "naive", "params": None, "plots": False},
-                              {"task": "forecast", "model": "exp-smoothing", "params": None, "plots": False}]
+                              {"task": "forecast", "model": "exp-smoothing", "params": None, "plots": False},
+                              {"task": "forecast", "model": "nbeats", "params": None, "plots": False}]
 
         for options in downstream_options:
             model = options.get("model")
diff --git a/tests/test_explainer.py b/tests/test_explainer.py
index ff7df0cf..244b4ced 100644
--- a/tests/test_explainer.py
+++ b/tests/test_explainer.py
@@ -22,7 +22,7 @@ def test_explainer_shap(self):
         ts_1 = TimeSeries()
         ts_1.load_series(utils.search_path(filename))
 
-        shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, file_name=filename, limit_ratio=0.3,
+        shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, file_name=filename, rate_dataset=0.3,
                                                              seed=True, verbose=True)
 
         self.assertTrue(shap_values is not None)
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index fc4a7a79..d954175b 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -42,7 +42,7 @@ def test_pipeline(self):
         # explainer
         ts_1 = TimeSeries()
         ts_1.load_series(utils.search_path("chlorine"))
-        shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, algorithm="cdrec", missing_rate=0.25, limit_ratio=0.4, split_ratio=0.6, file_name="eeg-alcohol")
+        shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, algorithm="cdrec", missing_rate=0.25, rate_dataset=0.4, training_ratio=0.6, file_name="eeg-alcohol")
         Explainer.print(shap_values, shap_details)
 
         # benchmark