.. _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([21966.69716963, 25983.70921139, 30225.30979747, 35417.12497254,
           13011.36188759, 19639.41029019, 21506.58177282, 23674.33999704,
           21685.76395507, 23669.77820646, 26955.35257496, 22755.2506303 ,
           19808.16134242, 23578.78187024, 27845.86722015, 32923.89428919,
           10475.68780117, 17024.74536224, 18831.64799954, 20929.54674345,
           18876.02417373, 20792.5751607 , 24011.97550244, 19745.03909889,
           16731.45366879, 20435.40474641, 24635.90942583, 29647.31034558,
            7132.5010055 , 13614.94378014, 15355.23774458, 17386.52468386,
           15266.39191381, 17116.33187861, 20269.12161919, 15935.57439881,
           12855.37826236, 16492.71857703, 20626.6125225 , 25571.40269344,
            2989.98261217,  9405.81464171, 11079.49786305, 13044.17405821,
           10857.43054456, 12640.7597655 , 15726.93876234, 11326.78079817,
            8179.97391796, 11750.70348884, 15817.98669054])




|


.. code-block:: python

    print(__doc__)

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

    import pyramid as pm
    from pyramid.datasets import load_wineind
    from sklearn.externals import joblib  # for persistence
    import os

    # #############################################################################
    # Load the data and split it into separate pieces
    y = load_wineind()
    train, test = y[:125], y[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  1.126 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>`_