.. _sphx_glr_auto_examples_example_simple_fit.py:


=======================
Simple auto_arima model
=======================


This is a simple example of how we can fit an ARIMA model in several lines
without knowing anything about our data or optimal hyper parameters.

.. raw:: html

   <br/>




.. image:: /auto_examples/images/sphx_glr_example_simple_fit_001.png
    :align: center


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

 Out::

    Performing stepwise search to minimize aic
     ARIMA(2,1,2)(1,0,1)[12] intercept   : AIC=2915.873, Time=1.00 sec
     ARIMA(0,1,0)(0,0,0)[12] intercept   : AIC=3049.597, Time=0.09 sec
     ARIMA(1,1,0)(1,0,0)[12] intercept   : AIC=2954.975, Time=0.30 sec
     ARIMA(0,1,1)(0,0,1)[12] intercept   : AIC=2947.028, Time=0.31 sec
     ARIMA(0,1,0)(0,0,0)[12]             : AIC=3047.612, Time=0.09 sec
     ARIMA(2,1,2)(0,0,1)[12] intercept   : AIC=2940.108, Time=0.51 sec
     ARIMA(2,1,2)(1,0,0)[12] intercept   : AIC=2915.651, Time=0.60 sec
     ARIMA(2,1,2)(0,0,0)[12] intercept   : AIC=2986.348, Time=0.12 sec
     ARIMA(2,1,2)(2,0,0)[12] intercept   : AIC=2915.275, Time=3.47 sec
     ARIMA(2,1,2)(2,0,1)[12] intercept   : AIC=2918.411, Time=4.71 sec
     ARIMA(1,1,2)(2,0,0)[12] intercept   : AIC=2925.080, Time=4.69 sec
     ARIMA(2,1,1)(2,0,0)[12] intercept   : AIC=2911.330, Time=4.50 sec
     ARIMA(2,1,1)(1,0,0)[12] intercept   : AIC=2911.555, Time=1.30 sec
     ARIMA(2,1,1)(2,0,1)[12] intercept   : AIC=2914.196, Time=4.20 sec
     ARIMA(2,1,1)(1,0,1)[12] intercept   : AIC=2912.289, Time=1.30 sec
     ARIMA(1,1,1)(2,0,0)[12] intercept   : AIC=2920.327, Time=3.20 sec
     ARIMA(2,1,0)(2,0,0)[12] intercept   : AIC=2928.273, Time=4.30 sec
     ARIMA(3,1,1)(2,0,0)[12] intercept   : AIC=2912.939, Time=4.70 sec
     ARIMA(1,1,0)(2,0,0)[12] intercept   : AIC=2954.804, Time=3.71 sec
     ARIMA(3,1,0)(2,0,0)[12] intercept   : AIC=2914.414, Time=4.81 sec
     ARIMA(3,1,2)(2,0,0)[12] intercept   : AIC=2915.331, Time=5.60 sec
     ARIMA(2,1,1)(2,0,0)[12]             : AIC=2908.221, Time=4.30 sec
     ARIMA(2,1,1)(1,0,0)[12]             : AIC=2909.011, Time=1.30 sec
     ARIMA(2,1,1)(2,0,1)[12]             : AIC=2910.144, Time=3.90 sec
     ARIMA(2,1,1)(1,0,1)[12]             : AIC=2908.093, Time=1.20 sec
     ARIMA(2,1,1)(0,0,1)[12]             : AIC=2933.343, Time=1.00 sec
     ARIMA(2,1,1)(1,0,2)[12]             : AIC=2910.039, Time=2.50 sec
     ARIMA(2,1,1)(0,0,0)[12]             : AIC=2980.096, Time=0.11 sec
     ARIMA(2,1,1)(0,0,2)[12]             : AIC=2921.090, Time=3.29 sec
     ARIMA(2,1,1)(2,0,2)[12]             : AIC=2912.081, Time=3.40 sec
     ARIMA(1,1,1)(1,0,1)[12]             : AIC=2915.531, Time=0.41 sec
     ARIMA(2,1,0)(1,0,1)[12]             : AIC=2925.551, Time=0.59 sec
     ARIMA(3,1,1)(1,0,1)[12]             : AIC=2911.034, Time=0.61 sec
     ARIMA(2,1,2)(1,0,1)[12]             : AIC=2913.397, Time=0.61 sec
     ARIMA(1,1,0)(1,0,1)[12]             : AIC=2952.580, Time=0.39 sec
     ARIMA(1,1,2)(1,0,1)[12]             : AIC=2922.527, Time=0.51 sec
     ARIMA(3,1,0)(1,0,1)[12]             : AIC=2912.590, Time=0.60 sec
     ARIMA(3,1,2)(1,0,1)[12]             : AIC=2913.867, Time=0.79 sec

    Best model:  ARIMA(2,1,1)(1,0,1)[12]          
    Total fit time: 79.584 seconds




|


.. code-block:: python

    print(__doc__)

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

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

    # #############################################################################
    # Load the data and split it into separate pieces
    data = pm.datasets.load_wineind()
    train, test = model_selection.train_test_split(data, train_size=150)

    # Fit a simple auto_arima model
    arima = pm.auto_arima(train, error_action='ignore', trace=True,
                          suppress_warnings=True, maxiter=5,
                          seasonal=True, m=12)

    # #############################################################################
    # Plot actual test vs. forecasts:
    x = np.arange(test.shape[0])
    plt.scatter(x, test, marker='x')
    plt.plot(x, arima.predict(n_periods=test.shape[0]))
    plt.title('Actual test samples vs. forecasts')
    plt.show()

**Total running time of the script:** ( 1 minutes  19.686 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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