Merge pull request #678 from JoaquinAmatRodrigo/0.12.x

0.12.x
skforecast · May 5, 2024 · a15dcbe · a15dcbe
2 parents af6daf8 + 9f0790e
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diff --git a/docs/user_guides/feature_selection.ipynb → docs/user_guides/feature-selection.ipynb b/docs/user_guides/feature_selection.ipynb → docs/user_guides/feature-selection.ipynb
@@ -866,7 +866,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Global Forecasting Models: Feature Selection\n",
+    "## Global Forecasting Models: Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "\n",
     "`select_features_multiseries` function from the `skforecast.model_selection_multiseries` module is used to select the best subset of features (autoregressive and exogenous variables). This function is compatible with the feature selection methods implemented in the scikit-learn library. The following parameters are available:\n",
     "\n",

diff --git a/mkdocs.yml b/mkdocs.yml
@@ -31,9 +31,9 @@ nav:
       - Input data: user_guides/input-data.ipynb
       - Recursive multi-step forecasting: user_guides/autoregresive-forecaster.ipynb
       - Direct multi-step forecasting: user_guides/direct-multi-step-forecasting.ipynb
-      - Independent multi-time series forecasting: user_guides/independent-multi-time-series-forecasting.ipynb
-      - Series with different lengths and different exogenous variables: user_guides/multi-series-with-different-length-and-different_exog.ipynb
-      - Dependent multivariate series forecasting: user_guides/dependent-multi-series-multivariate-forecasting.ipynb
+      - "Global Models : Independent multi-time series forecasting": user_guides/independent-multi-time-series-forecasting.ipynb
+      - "Global Models : Series with different lengths and different exogenous variables": user_guides/multi-series-with-different-length-and-different_exog.ipynb
+      - "Global Models : Dependent multivariate series forecasting": user_guides/dependent-multi-series-multivariate-forecasting.ipynb
       - Deep learning Recurrent Neural Networks: user_guides/forecasting-with-deep-learning-rnn-lstm.ipynb
       - ARIMA and SARIMAX forecasting: user_guides/forecasting-sarimax-arima.ipynb
       - Foreasting baseline: user_guides/forecasting-baseline.ipynb

diff --git a/skforecast/ForecasterAutoreg/ForecasterAutoreg.py b/skforecast/ForecasterAutoreg/ForecasterAutoreg.py
@@ -889,6 +889,9 @@ def predict_bootstrapping(
             If `True`, residuals used in each bootstrapping iteration are selected
             conditioning on the predicted values. If `False`, residuals are selected
             randomly without conditioning on the predicted values.
+            **WARNING: This argument is newly introduced and requires special attention.
+            It is still experimental and may undergo changes.**
+            **New in version 0.12.0**
 
         Returns
         -------
@@ -1100,6 +1103,9 @@ def predict_interval(
             If `True`, residuals used in each bootstrapping iteration are selected
             conditioning on the predicted values. If `False`, residuals are selected
             randomly without conditioning on the predicted values.
+            **WARNING: This argument is newly introduced and requires special attention.
+            It is still experimental and may undergo changes.**
+            **New in version 0.12.0**
 
         Returns
         -------
@@ -1192,6 +1198,9 @@ def predict_quantiles(
             If `True`, residuals used in each bootstrapping iteration are selected
             conditioning on the predicted values. If `False`, residuals are selected
             randomly without conditioning on the predicted values.
+            **WARNING: This argument is newly introduced and requires special attention.
+            It is still experimental and may undergo changes.**
+            **New in version 0.12.0**
 
         Returns
         -------
@@ -1270,6 +1279,9 @@ def predict_dist(
             If `True`, residuals used in each bootstrapping iteration are selected
             conditioning on the predicted values. If `False`, residuals are selected
             randomly without conditioning on the predicted values.
+            **WARNING: This argument is newly introduced and requires special attention.
+            It is still experimental and may undergo changes.**
+            **New in version 0.12.0**
 
         Returns
         -------