.. _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/>




.. image:: /auto_examples/model_selection/images/sphx_glr_example_cross_val_predict_001.png
    :align: center


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

 Out::

    pmdarima version: 0.0.0
    [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()

**Total running time of the script:** ( 0 minutes  36.068 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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