.. _sphx_glr_auto_examples_arima_example_persisting_a_model.py:


=========================
Persisting an ARIMA model
=========================


This example demonstrates how we can persist an ARIMA model to disk after
fitting it. It can then be loaded back up and used to generate forecasts.

.. raw:: html

   <br/>





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

 Out::

    Predictions: array([21966.69715674, 25983.70919719, 30225.30978371, 35417.12495804,
           13011.36187769, 19639.41027228, 21506.58175753, 23674.33997867,
           21685.76393805, 23669.778184  , 26955.35255653, 22755.25061482,
           19808.16131817, 23578.78184187, 27845.86719195, 32923.89426059,
           10475.68777621, 17024.74532948, 18831.64796876, 20929.54670952,
           18876.02414073, 20792.57512203, 24011.97546746, 19745.0390666 ,
           16731.45362742, 20435.40470062, 24635.90937992, 29647.31029897,
            7132.50096224, 13614.94372878, 15355.23769491, 17386.52463074,
           15266.39186131, 17116.33182016, 20269.12156413, 15935.57434614,
           12855.37820032, 16492.71851028, 20626.61245533, 25571.40262528,
            2989.98254705,  9405.81456821, 11079.49779094, 13044.17398235,
           10857.43046903, 12640.75968372, 15726.93868366, 11326.78072158,
            8179.9738317 , 11750.70339758, 15817.98659857])




|


.. code-block:: python

    print(__doc__)

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

    import pmdarima as pm
    from pmdarima import model_selection
    import joblib  # for persistence
    import os

    # #############################################################################
    # Load the data and split it into separate pieces
    y = pm.datasets.load_wineind()
    train, test = model_selection.train_test_split(y, train_size=125)

    # Fit an ARIMA
    arima = pm.ARIMA(order=(1, 1, 2), seasonal_order=(0, 1, 1, 12))
    arima.fit(y)

    # #############################################################################
    # Persist a model and create predictions after re-loading it
    pickle_tgt = "arima.pkl"
    try:
        # Pickle it
        joblib.dump(arima, pickle_tgt, compress=3)

        # Load the model up, create predictions
        arima_loaded = joblib.load(pickle_tgt)
        preds = arima_loaded.predict(n_periods=test.shape[0])
        print("Predictions: %r" % preds)

    finally:
        # Remove the pickle file at the end of this example
        try:
            os.unlink(pickle_tgt)
        except OSError:
            pass

**Total running time of the script:** ( 0 minutes  3.320 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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