.. _sphx_glr_auto_examples_plot_paa.py:


=================================
Piecewise Aggregate Approximation
=================================

This example shows how you can approximate a time series using
:class:`pyts.approximation.PAA`.




.. image:: /auto_examples/images/sphx_glr_plot_paa_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.approximation import PAA

    # Parameters
    n_samples, n_features = 100, 48

    # Toy dataset
    rng = np.random.RandomState(41)
    X = rng.randn(n_samples, n_features)

    # PAA transformation
    window_size = 6
    paa = PAA(window_size=window_size)
    X_paa = paa.transform(X)

    # Show the results for the first time series
    plt.figure(figsize=(12, 8))
    plt.plot(np.arange(n_features), X[0], 'o-', label='Original')
    plt.plot(np.arange(window_size // 2,
                       n_features + window_size // 2,
                       window_size), X_paa[0], 'o--', label='PAA')
    plt.vlines(np.arange(0, n_features, window_size),
               X[0].min(), X[0].max(), color='g', linestyles='--', linewidth=0.5)
    plt.legend(loc='best', fontsize=14)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.040 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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