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‎Chapter5/spark.ipynb

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‎Chapter5/time_series.ipynb

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"outputs": [
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"4 H1 5 511.0"
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]
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},
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{
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},
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"<Figure size 800x300 with 1 Axes>"
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]
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},
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"outputs": [
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"MLForecast(models=[LinearRegression, KNeighborsRegressor], freq=1, lag_features=['lag24', 'lag48', 'lag72', 'lag96', 'lag120', 'lag144', 'lag168'], date_features=[], num_threads=1)"
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"metadata": {},
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"source": [
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"mlf.fit(\n",
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" data=train,\n",
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" df=train,\n",
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" prediction_intervals=PredictionIntervals(n_windows=10, h=48),\n",
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")"
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"4 491.92 508.645 "
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‎Chapter5/visualization.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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"version": "3.11.4"
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"toc": {
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‎docs/Chapter5/spark.html

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‎docs/Chapter5/time_series.html

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<div class="cell docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mlf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
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<span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span>
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<span class="n">df</span><span class="o">=</span><span class="n">train</span><span class="p">,</span>
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<span class="n">prediction_intervals</span><span class="o">=</span><span class="n">PredictionIntervals</span><span class="p">(</span><span class="n">n_windows</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">h</span><span class="o">=</span><span class="mi">48</span><span class="p">),</span>
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</pre></div>

‎docs/_sources/Chapter5/spark.ipynb

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‎docs/_sources/Chapter5/time_series.ipynb

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"4 H1 5 511.0"
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"<Figure size 800x300 with 1 Axes>"
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"MLForecast(models=[LinearRegression, KNeighborsRegressor], freq=1, lag_features=['lag24', 'lag48', 'lag72', 'lag96', 'lag120', 'lag144', 'lag168'], date_features=[], num_threads=1)"
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"4 491.92 508.645 "
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‎docs/_sources/Chapter5/visualization.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"version": "3.11.6"
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"version": "3.11.4"
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"toc": {
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‎docs/searchindex.js

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