.. _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=2915.641, Time=0.37 sec
     ARIMA(0,1,0)(0,0,0)[12] intercept   : AIC=3049.597, Time=0.01 sec
     ARIMA(1,1,0)(1,0,0)[12] intercept   : AIC=2954.973, Time=0.23 sec
     ARIMA(0,1,1)(0,0,1)[12] intercept   : AIC=2947.014, Time=0.25 sec
     ARIMA(0,1,0)(0,0,0)[12]             : AIC=3047.612, Time=0.01 sec
     ARIMA(2,1,2)(0,0,1)[12] intercept   : AIC=2938.457, Time=0.40 sec
     ARIMA(2,1,2)(1,0,0)[12] intercept   : AIC=2915.636, Time=0.36 sec
     ARIMA(2,1,2)(0,0,0)[12] intercept   : AIC=2984.606, Time=0.10 sec
     ARIMA(2,1,2)(2,0,0)[12] intercept   : AIC=2915.168, Time=3.81 sec
     ARIMA(2,1,2)(2,0,1)[12] intercept   : AIC=2917.256, Time=3.69 sec
     ARIMA(1,1,2)(2,0,0)[12] intercept   : AIC=2924.379, Time=3.23 sec
     ARIMA(2,1,1)(2,0,0)[12] intercept   : AIC=2911.007, Time=3.77 sec
     ARIMA(2,1,1)(1,0,0)[12] intercept   : AIC=2911.505, Time=0.35 sec
     ARIMA(2,1,1)(2,0,1)[12] intercept   : AIC=2912.933, Time=3.54 sec
     ARIMA(2,1,1)(1,0,1)[12] intercept   : AIC=2911.904, Time=0.26 sec
     ARIMA(1,1,1)(2,0,0)[12] intercept   : AIC=2919.168, Time=1.68 sec
     ARIMA(2,1,0)(2,0,0)[12] intercept   : AIC=2927.636, Time=3.07 sec
     ARIMA(3,1,1)(2,0,0)[12] intercept   : AIC=2912.748, Time=5.72 sec
     ARIMA(1,1,0)(2,0,0)[12] intercept   : AIC=2954.725, Time=2.51 sec
     ARIMA(3,1,0)(2,0,0)[12] intercept   : AIC=2914.169, Time=3.70 sec
     ARIMA(3,1,2)(2,0,0)[12] intercept   : AIC=2915.130, Time=3.36 sec
     ARIMA(2,1,1)(2,0,0)[12]             : AIC=2908.085, Time=2.34 sec
     ARIMA(2,1,1)(1,0,0)[12]             : AIC=2908.791, Time=0.31 sec
     ARIMA(2,1,1)(2,0,1)[12]             : AIC=2909.997, Time=2.76 sec
     ARIMA(2,1,1)(1,0,1)[12]             : AIC=2907.980, Time=0.32 sec
     ARIMA(2,1,1)(0,0,1)[12]             : AIC=2932.875, Time=0.32 sec
     ARIMA(2,1,1)(1,0,2)[12]             : AIC=2909.946, Time=7.37 sec
     ARIMA(2,1,1)(0,0,0)[12]             : AIC=2979.838, Time=0.11 sec
     ARIMA(2,1,1)(0,0,2)[12]             : AIC=2920.616, Time=3.41 sec
     ARIMA(2,1,1)(2,0,2)[12]             : AIC=2911.929, Time=7.60 sec
     ARIMA(1,1,1)(1,0,1)[12]             : AIC=2915.102, Time=0.41 sec
     ARIMA(2,1,0)(1,0,1)[12]             : AIC=2925.280, Time=0.35 sec
     ARIMA(3,1,1)(1,0,1)[12]             : AIC=2910.007, Time=1.14 sec
     ARIMA(2,1,2)(1,0,1)[12]             : AIC=2913.030, Time=0.62 sec
     ARIMA(1,1,0)(1,0,1)[12]             : AIC=2952.472, Time=0.31 sec
     ARIMA(1,1,2)(1,0,1)[12]             : AIC=2922.341, Time=0.47 sec
     ARIMA(3,1,0)(1,0,1)[12]             : AIC=2912.111, Time=0.37 sec
     ARIMA(3,1,2)(1,0,1)[12]             : AIC=2913.230, Time=0.49 sec

    Best model:  ARIMA(2,1,1)(1,0,1)[12]          
    Total fit time: 69.155 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=10,
                          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  9.181 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>`_