.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/model_selection/example_cross_val_predict.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_model_selection_example_cross_val_predict.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_model_selection_example_cross_val_predict.py:


============================
Cross-validation predictions
============================

In addition to computing cross-validation scores, you can use cross-validation
to produce predictions. Unlike traditional cross-validation, where folds are
independent of one another, time-series folds may overlap (particularly in a
sliding window). To account for this, folds that forecast the same time step
average their forecasts using either a "mean" or "median" (tunable).

.. raw:: html

   <br/>

.. GENERATED FROM PYTHON SOURCE LINES 16-44



.. image-sg:: /auto_examples/model_selection/images/sphx_glr_example_cross_val_predict_001.png
   :alt: Cross-validated wineind forecasts
   :srcset: /auto_examples/model_selection/images/sphx_glr_example_cross_val_predict_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    pmdarima version: 2.1.0
    [CV] fold=0 ..........................................................
    [CV] fold=1 ..........................................................
    [CV] fold=2 ..........................................................
    [CV] fold=3 ..........................................................
    [CV] fold=4 ..........................................................
    [CV] fold=5 ..........................................................






|

.. code-block:: Python

    print(__doc__)

    # Author: Taylor Smith <taylor.smith@alkaline-ml.com>

    import numpy as np
    import pmdarima as pm
    from pmdarima import model_selection
    from matplotlib import pyplot as plt

    print("pmdarima version: %s" % pm.__version__)

    # Load the data and split it into separate pieces
    y = pm.datasets.load_wineind()
    est = pm.ARIMA(order=(1, 1, 2),
                   seasonal_order=(0, 1, 1, 12),
                   suppress_warnings=True)
    cv = model_selection.SlidingWindowForecastCV(window_size=150, step=4, h=4)
    predictions = model_selection.cross_val_predict(
        est, y, cv=cv, verbose=2, averaging="median")

    # plot the predictions over the original series
    x_axis = np.arange(y.shape[0])
    n_test = predictions.shape[0]

    plt.plot(x_axis, y, alpha=0.75, c='b')
    plt.plot(x_axis[-n_test:], predictions, alpha=0.75, c='g')  # Forecasts
    plt.title("Cross-validated wineind forecasts")
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 34.291 seconds)


.. _sphx_glr_download_auto_examples_model_selection_example_cross_val_predict.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: example_cross_val_predict.ipynb <example_cross_val_predict.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: example_cross_val_predict.py <example_cross_val_predict.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: example_cross_val_predict.zip <example_cross_val_predict.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_