.. _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([21966.6971736 , 25983.70921864, 30225.30980326, 35417.124979  ,
           13011.36189291, 19639.41029709, 21506.58177915, 23674.34000393,
           21685.76396166, 23669.77821406, 26955.35258175, 22755.25063648,
           19808.16135114, 23578.78188153, 27845.86723055, 32923.89429994,
           10475.68781103, 17024.74537358, 18831.64801043, 20929.54675489,
           18876.02418493, 20792.57517292, 24011.9755139 , 19745.03910977,
           16731.45368225, 20435.40476246, 24635.90944103, 29647.31036116,
            7132.50102023, 13614.94379639, 15355.2377604 , 17386.52470027,
           15266.39193001, 17116.33189588, 20269.12163572, 15935.5744148 ,
           12855.37828096, 16492.71859826, 20626.61254291, 25571.40271427,
            2989.98263217,  9405.81466327, 11079.49788422, 13044.17408   ,
           10857.43056618, 12640.75978821, 15726.93878436, 11326.78081968,
            8179.97394211, 11750.70351566, 15817.98671658])




|


.. 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  8.489 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>`_