.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/arima/example_auto_arima.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_arima_example_auto_arima.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_arima_example_auto_arima.py:


===========================
Fitting an auto_arima model
===========================


This example demonstrates how we can use the ``auto_arima`` function to
select an optimal time series model. We'll be fitting our model on the lynx
dataset available in the :ref:`datasets` submodule.

.. raw:: html

   <br/>

.. GENERATED FROM PYTHON SOURCE LINES 15-56



.. image-sg:: /auto_examples/arima/images/sphx_glr_example_auto_arima_001.png
   :alt: Lynx forecasts
   :srcset: /auto_examples/arima/images/sphx_glr_example_auto_arima_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Test RMSE: 1258.625

    Text(0.5, 23.52222222222222, 'Year')





|

.. code-block:: Python

    print(__doc__)

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

    import pmdarima as pm
    from pmdarima import model_selection
    from sklearn.metrics import mean_squared_error
    import matplotlib.pyplot as plt
    import numpy as np

    # #############################################################################
    # Load the data and split it into separate pieces
    data = pm.datasets.load_lynx()
    train, test = model_selection.train_test_split(data, train_size=90)

    # Fit a simple auto_arima model
    modl = pm.auto_arima(train, start_p=1, start_q=1, start_P=1, start_Q=1,
                         max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=True,
                         stepwise=True, suppress_warnings=True, D=10, max_D=10,
                         error_action='ignore')

    # Create predictions for the future, evaluate on test
    preds, conf_int = modl.predict(n_periods=test.shape[0], return_conf_int=True)

    # Print the error:
    print("Test RMSE: %.3f" % np.sqrt(mean_squared_error(test, preds)))

    # #############################################################################
    # Plot the points and the forecasts
    x_axis = np.arange(train.shape[0] + preds.shape[0])
    x_years = x_axis + 1821  # Year starts at 1821

    plt.plot(x_years[x_axis[:train.shape[0]]], train, alpha=0.75)
    plt.plot(x_years[x_axis[train.shape[0]:]], preds, alpha=0.75)  # Forecasts
    plt.scatter(x_years[x_axis[train.shape[0]:]], test,
                alpha=0.4, marker='x')  # Test data
    plt.fill_between(x_years[x_axis[-preds.shape[0]:]],
                     conf_int[:, 0], conf_int[:, 1],
                     alpha=0.1, color='b')
    plt.title("Lynx forecasts")
    plt.xlabel("Year")


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.044 seconds)


.. _sphx_glr_download_auto_examples_arima_example_auto_arima.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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

    .. container:: sphx-glr-download sphx-glr-download-python

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: example_auto_arima.zip <example_auto_arima.zip>`


.. only:: html

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

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