.. _sphx_glr_auto_examples_arima_example_add_new_samples.py:


=====================================
Adding new observations to your model
=====================================


This example demonstrates how to add new ground truth
observations to your model so that forecasting continues
with respect to true, observed values. This also slightly
updates the model parameters, taking several new steps from
the existing model parameters.

.. raw:: html

   <br/>




.. image:: /auto_examples/arima/images/sphx_glr_example_add_new_samples_001.png
    :align: center





.. code-block:: python

    print(__doc__)

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

    import pmdarima as pm
    from pmdarima import model_selection
    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=100)

    # #############################################################################
    # Fit with some validation (cv) samples
    arima = pm.auto_arima(train, start_p=1, start_q=1, d=0, max_p=5, max_q=5,
                          out_of_sample_size=10, suppress_warnings=True,
                          stepwise=True, error_action='ignore')

    # Now plot the results and the forecast for the test set
    preds, conf_int = arima.predict(n_periods=test.shape[0],
                                    return_conf_int=True)

    fig, axes = plt.subplots(2, 1, figsize=(12, 8))
    x_axis = np.arange(train.shape[0] + preds.shape[0])
    axes[0].plot(x_axis[:train.shape[0]], train, alpha=0.75)
    axes[0].scatter(x_axis[train.shape[0]:], preds, alpha=0.4, marker='o')
    axes[0].scatter(x_axis[train.shape[0]:], test, alpha=0.4, marker='x')
    axes[0].fill_between(x_axis[-preds.shape[0]:], conf_int[:, 0], conf_int[:, 1],
                         alpha=0.1, color='b')

    # fill the section where we "held out" samples in our model fit

    axes[0].set_title("Train samples & forecasted test samples")

    # Now add the actual samples to the model and create NEW forecasts
    arima.update(test)
    new_preds, new_conf_int = arima.predict(n_periods=10, return_conf_int=True)
    new_x_axis = np.arange(data.shape[0] + 10)

    axes[1].plot(new_x_axis[:data.shape[0]], data, alpha=0.75)
    axes[1].scatter(new_x_axis[data.shape[0]:], new_preds, alpha=0.4, marker='o')
    axes[1].fill_between(new_x_axis[-new_preds.shape[0]:],
                         new_conf_int[:, 0],
                         new_conf_int[:, 1],
                         alpha=0.1, color='g')
    axes[1].set_title("Added new observed values with new forecasts")
    plt.show()

**Total running time of the script:** ( 0 minutes  0.564 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


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

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