.. _sphx_glr_auto_examples_plot_ssa.py:


==========================
Singular Spectrum Analysis
==========================

This example shows how you can decompose a time series into several time series
using :class:`pyts.decomposition.SSA`.




.. image:: /auto_examples/images/sphx_glr_plot_ssa_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.decomposition import SSA

    # Parameters
    n_samples, n_features = 100, 48

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

    # SSA transformation
    window_size = 15
    grouping = [[0, 1]]
    ssa = SSA(window_size, grouping)
    X_ssa = ssa.fit_transform(X)

    # Show the results for the first time series
    plt.figure(figsize=(12, 8))
    plt.plot(X[0], 'o-', label='Original')
    plt.plot(X_ssa[0, 0], 'o--', label='SSA')
    plt.legend(loc='best', fontsize=14)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.357 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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