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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_approximation_plot_paa.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_approximation_plot_paa.py:


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

Time series with a high sampling rate can be very noisy. In order to reduce
noise, a technique called *Piecewise Aggregate Approximation* was invented,
consisting in taking the mean over back-to-back points. This decreases the
number of points and reduces noise while preserving the trend of the time
series. This example illustrates the transformation.
It is implemented as
:class:`pyts.approximation.PiecewiseAggregateApproximation`.

.. GENERATED FROM PYTHON SOURCE LINES 14-46



.. image-sg:: /auto_examples/approximation/images/sphx_glr_plot_paa_001.png
   :alt: Piecewise Aggregate Approximation
   :srcset: /auto_examples/approximation/images/sphx_glr_plot_paa_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    # Author: Johann Faouzi <johann.faouzi@gmail.com>
    # License: BSD-3-Clause

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

    # Parameters
    n_samples, n_timestamps = 100, 48

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

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

    # Show the results for the first time series
    plt.figure(figsize=(6, 4))
    plt.plot(X[0], 'o--', ms=4, label='Original')
    plt.plot(np.arange(window_size // 2,
                       n_timestamps + window_size // 2,
                       window_size), X_paa[0], 'o--', ms=4, label='PAA')
    plt.vlines(np.arange(0, n_timestamps, window_size) - 0.5,
               X[0].min(), X[0].max(), color='g', linestyles='--', linewidth=0.5)
    plt.legend(loc='best', fontsize=10)
    plt.xlabel('Time', fontsize=12)
    plt.title('Piecewise Aggregate Approximation', fontsize=16)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.430 seconds)


.. _sphx_glr_download_auto_examples_approximation_plot_paa.py:

.. only:: html

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


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

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

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

      :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.github.io>`_