fix broken links

Author: deven367

README.md

@@ -32,15 +32,15 @@ data using remote clusters.
 `conda install -c conda-forge mlforecast`
 
 For more detailed instructions you can refer to the [installation
-page](https://nixtla.github.io/mlforecast/docs/getting-started/install.html).
+page](https://nixtlaverse.nixtla.io/mlforecast/docs/getting-started/install).
 
 ## Quick Start
 
 **Get Started with this [quick
-guide](https://nixtla.github.io/mlforecast/docs/getting-started/quick_start_local.html).**
+guide](https://nixtlaverse.github.io/mlforecast/docs/getting-started/quick_start_local.html).**
 
 **Follow this [end-to-end
-walkthrough](https://nixtla.github.io/mlforecast/docs/getting-started/end_to_end_walkthrough.html)
+walkthrough](https://nixtlaverse.nixtla.io/mlforecast/docs/getting-started/end_to_end_walkthrough)
 for best practices.**
 
 ### Videos
@@ -61,7 +61,7 @@ for best practices.**
 Current Python alternatives for machine learning models are slow,
 inaccurate and don’t scale well. So we created a library that can be
 used to forecast in production environments.
-[`MLForecast`](https://Nixtla.github.io/mlforecast/forecast.html#mlforecast)
+[`MLForecast`](https://nixtlaverse.nixtla.io/mlforecast/forecast#class-mlforecast)
 includes efficient feature engineering to train any machine learning
 model (with `fit` and `predict` methods such as
 [`sklearn`](https://scikit-learn.org/stable/)) to fit millions of time
@@ -83,36 +83,36 @@ Missing something? Please open an issue or write us in
 ## Examples and Guides
 
 📚 [End to End
-Walkthrough](https://nixtla.github.io/mlforecast/docs/getting-started/end_to_end_walkthrough.html):
+Walkthrough](https://nixtlaverse.nixtla.io/mlforecast/docs/getting-started/end_to_end_walkthrough):
 model training, evaluation and selection for multiple time series.
 
 🔎 [Probabilistic
-Forecasting](https://nixtla.github.io/mlforecast/docs/how-to-guides/prediction_intervals.html):
+Forecasting](https://nixtlaverse.nixtla.io/mlforecast/docs/tutorials/prediction_intervals_in_forecasting_models):
 use Conformal Prediction to produce prediciton intervals.
 
 👩‍🔬 [Cross
-Validation](https://nixtla.github.io/mlforecast/docs/how-to-guides/cross_validation.html):
+Validation](https://nixtlaverse.nixtla.io/mlforecast/docs/how-to-guides/cross_validation):
 robust model’s performance evaluation.
 
 🔌 [Predict Demand
-Peaks](https://nixtla.github.io/mlforecast/docs/tutorials/electricity_peak_forecasting.html):
+Peaks](https://nixtlaverse.nixtla.io/mlforecast/docs/tutorials/electricity_peak_forecasting):
 electricity load forecasting for detecting daily peaks and reducing
 electric bills.
 
 📈 [Transfer
-Learning](https://nixtla.github.io/mlforecast/docs/how-to-guides/transfer_learning.html):
+Learning](https://nixtlaverse.nixtla.io/mlforecast/docs/how-to-guides/transfer_learning):
 pretrain a model using a set of time series and then predict another one
 using that pretrained model.
 
 🌡️ [Distributed
-Training](https://nixtla.github.io/mlforecast/docs/getting-started/quick_start_distributed.html):
+Training](https://nixtlaverse.nixtla.io/mlforecast/docs/getting-started/quick_start_distributed):
 use a Dask, Ray or Spark cluster to train models at scale.
 
 ## How to use
 
 The following provides a very basic overview, for a more detailed
 description see the
-[documentation](https://nixtla.github.io/mlforecast/).
+[documentation](https://nixtlaverse.nixtla.io/mlforecast/).
 
 ### Data setup
 
@@ -165,7 +165,7 @@ models = [
 ### Forecast object
 
 Now instantiate an
-[`MLForecast`](https://Nixtla.github.io/mlforecast/forecast.html#mlforecast)
+[`MLForecast`](https://nixtlaverse.nixtla.io/mlforecast/forecast#class-mlforecast)
 object with the models and the features that you want to use. The
 features can be lags, transformations on the lags and date features. You
 can also define transformations to apply to the target before fitting,

nbs/docs/how-to-guides/cross_validation.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "## Prerequesites\n",
     "\n",
-    "This tutorial assumes basic familiarity with `MLForecast`. For a minimal example visit the [Quick Start](quick_start_local.html) \n",
+    "This tutorial assumes basic familiarity with `MLForecast`. For a minimal example visit the [Quick Start](https://nixtlaverse.nixtla.io/mlforecast/docs/getting-started/quick_start_local)\n",
     ":::"
    ]
   },
@@ -36,7 +36,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[MLForecast](https://nixtla.github.io/mlforecast/) has an implementation of time series cross-validation that is fast and easy to use. This implementation makes cross-validation a efficient operation, which makes it less time-consuming. In this notebook, we'll use it on a subset of the [M4 Competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128) hourly dataset. "
+    "[MLForecast](https://nixtlaverse.nixtla.io/mlforecast/) has an implementation of time series cross-validation that is fast and easy to use. This implementation makes cross-validation a efficient operation, which makes it less time-consuming. In this notebook, we'll use it on a subset of the [M4 Competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128) hourly dataset. "
    ]
   },
   {
@@ -72,7 +72,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We assume that you have `MLForecast` already installed. If not, check this guide for instructions on [how to install MLForecast](../getting-started/install.html)."
+    "We assume that you have `MLForecast` already installed. If not, check this guide for instructions on [how to install MLForecast](https://nixtlaverse.nixtla.io/mlforecast/docs/getting-started/install)"
    ]
   },
   {
@@ -88,11 +88,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd \n",
+    "import pandas as pd\n",
     "\n",
     "from utilsforecast.plotting import plot_series\n",
     "\n",
-    "from mlforecast import MLForecast # required to instantiate MLForecast object and use cross-validation method "
+    "from mlforecast import MLForecast # required to instantiate MLForecast object and use cross-validation method"
    ]
   },
   {
@@ -190,8 +190,8 @@
     }
    ],
    "source": [
-    "Y_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/m4-hourly.csv') # load the data \n",
-    "Y_df.head() "
+    "Y_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/m4-hourly.csv') # load the data\n",
+    "Y_df.head()"
    ]
   },
   {
@@ -252,9 +252,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For this example, we'll use LightGBM. We first need to import it and then we need to instantiate a new [MLForecast](../../forecast.html#mlforecast) object. \n",
+    "For this example, we'll use LightGBM. We first need to import it and then we need to instantiate a new [MLForecast](https://nixtlaverse.nixtla.io/mlforecast/forecast#class-mlforecast) object. \n",
     "\n",
-    "In this example, we are only using `differences` and `lags` to produce features. See [the full documentation](https://nixtla.github.io/mlforecast) to see all available features.\n",
+    "In this example, we are only using `differences` and `lags` to produce features. See [the full documentation](https://nixtlaverse.nixtla.io/mlforecast) to see all available features.\n",
     "\n",
     "Any settings are passed into the constructor. Then you call its `fit` method and pass in the historical data frame `df`. "
    ]
@@ -278,8 +278,8 @@
     "models = [lgb.LGBMRegressor(verbosity=-1)]\n",
     "\n",
     "mlf = MLForecast(\n",
-    "    models=models, \n",
-    "    freq=1,# our series have integer timestamps, so we'll just add 1 in every timeste, \n",
+    "    models=models,\n",
+    "    freq=1,# our series have integer timestamps, so we'll just add 1 in every timeste,\n",
     "    target_transforms=[Differences([24])],\n",
     "    lags=range(1, 25)\n",
     ")"
@@ -296,7 +296,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once the `MLForecast` object has been instantiated, we can use the [cross_validation method](../../forecast.html#mlforecast.cross_validation)."
+    "Once the `MLForecast` object has been instantiated, we can use the [cross_validation method](https://nixtlaverse.nixtla.io/mlforecast/forecast#method-cross-validation)"
    ]
   },
   {
@@ -504,7 +504,7 @@
    "outputs": [],
    "source": [
     "from utilsforecast.evaluation import evaluate\n",
-    "from utilsforecast.losses import rmse "
+    "from utilsforecast.losses import rmse"
    ]
   },
   {

nbs/docs/how-to-guides/prediction_intervals.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "## Prerequesites\n",
     "\n",
-    "This tutorial assumes basic familiarity with MLForecast. For a minimal example visit the [Quick Start](https://nixtla.github.io/mlforecast/docs/quick_start_local.html) \n",
+    "This tutorial assumes basic familiarity with MLForecast. For a minimal example visit the [Quick Start](https://nixtlaverse.nixtla.io/mlforecast/docs/getting-started/quick_start_local)\n",
     ":::"
    ]
   },
@@ -323,7 +323,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "n_series = 8 \n",
+    "n_series = 8\n",
     "uids = train['unique_id'].unique()[:n_series] # select first n_series of the dataset\n",
     "train = train.query('unique_id in @uids')\n",
     "test = test.query('unique_id in @uids')"
@@ -415,7 +415,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create a list of models and instantiation parameters \n",
+    "# Create a list of models and instantiation parameters\n",
     "models = [\n",
     "    KNeighborsRegressor(),\n",
     "    Lasso(),\n",
@@ -765,11 +765,11 @@
    "outputs": [],
    "source": [
     "fig = plot_series(\n",
-    "    train, \n",
-    "    test, \n",
-    "    plot_random=False, \n",
-    "    models=['KNeighborsRegressor'], \n",
-    "    level=levels, \n",
+    "    train,\n",
+    "    test,\n",
+    "    plot_random=False,\n",
+    "    models=['KNeighborsRegressor'],\n",
+    "    level=levels,\n",
     "    max_insample_length=48\n",
     ")"
    ]
@@ -809,11 +809,11 @@
    "outputs": [],
    "source": [
     "fig = plot_series(\n",
-    "    train, \n",
-    "    test, \n",
-    "    plot_random=False, \n",
+    "    train,\n",
+    "    test,\n",
+    "    plot_random=False,\n",
     "    models=['Lasso'],\n",
-    "    level=levels, \n",
+    "    level=levels,\n",
     "    max_insample_length=48\n",
     ")"
    ]
@@ -853,11 +853,11 @@
    "outputs": [],
    "source": [
     "fig = plot_series(\n",
-    "    train, \n",
-    "    test, \n",
-    "    plot_random=False, \n",
+    "    train,\n",
+    "    test,\n",
+    "    plot_random=False,\n",
     "    models=['LinearRegression'],\n",
-    "    level=levels, \n",
+    "    level=levels,\n",
     "    max_insample_length=48\n",
     ")"
    ]
@@ -897,11 +897,11 @@
    "outputs": [],
    "source": [
     "fig = plot_series(\n",
-    "    train, \n",
-    "    test, \n",
-    "    plot_random=False, \n",
+    "    train,\n",
+    "    test,\n",
+    "    plot_random=False,\n",
     "    models=['MLPRegressor'],\n",
-    "    level=levels, \n",
+    "    level=levels,\n",
     "    max_insample_length=48\n",
     ")"
    ]
@@ -941,11 +941,11 @@
    "outputs": [],
    "source": [
     "fig = plot_series(\n",
-    "    train, \n",
-    "    test, \n",
-    "    plot_random=False, \n",
+    "    train,\n",
+    "    test,\n",
+    "    plot_random=False,\n",
     "    models=['Ridge'],\n",
-    "    level=levels, \n",
+    "    level=levels,\n",
     "    max_insample_length=48\n",
     ")"
    ]

nbs/docs/how-to-guides/transfer_learning.ipynb

@@ -153,7 +153,7 @@
     "- `differences`: Differences to take of the target before computing the features. These are restored at the forecasting step.\n",
     "- `lags`: Lags of the target to use as features.\n",
     "\n",
-    "In this example, we are only using `differences` and `lags` to produce features. See [the full documentation](https://nixtla.github.io/mlforecast/forecast.html) to see all available features.\n",
+    "In this example, we are only using `differences` and `lags` to produce features. See [the full documentation](https://nixtlaverse.nixtla.io/mlforecast/forecast) to see all available features.\n",
     "\n",
     "Any settings are passed into the constructor. Then you call its `fit` method and pass in the historical data frame `Y_df_M3`. "
    ]
@@ -165,7 +165,7 @@
    "outputs": [],
    "source": [
     "fcst = MLForecast(\n",
-    "    models=models, \n",
+    "    models=models,\n",
     "    lags=range(1, 13),\n",
     "    freq='MS',\n",
     "    target_transforms=[Differences([1, 12])],\n",
@@ -190,7 +190,7 @@
    "source": [
     "Y_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/air-passengers.csv', parse_dates=['ds'])\n",
     "\n",
-    "# We define the train df. \n",
+    "# We define the train df.\n",
     "Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train\n",
     "Y_test_df = Y_df[Y_df.ds>'1959-12-31']   # 12 test"
    ]