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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_preprocessing_plot_imputer.py:


=======
Imputer
=======

Missing values are common in real-word datasets and most algorithms cannot
deal with them. Thus it is standard to impute them. For time series, the
imputation is based on interpolation from other time points in order to
preserve temporal correlation between consecutive time points. Different
strategies for interpolation are made available. This example illustrates
these different strategies.
It is implemented as :class:`pyts.preprocessing.InterpolationImputer`.

.. GENERATED FROM PYTHON SOURCE LINES 14-48



.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_imputer_001.png
   :alt: Interpolating missing values with different strategies, Linear Interpolation, Quadratic Interpolation, Cubic Interpolation, Nearest Interpolation
   :srcset: /auto_examples/preprocessing/images/sphx_glr_plot_imputer_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.preprocessing import InterpolationImputer

    # Parameters
    n_samples, n_timestamps = 100, 48

    # Toy dataset
    rng = np.random.RandomState(41)
    X = rng.randn(n_samples, n_timestamps)
    missing_idx = rng.choice(np.arange(1, 47), size=14, replace=False)
    X[:, missing_idx] = np.nan

    # Show the results for different strategies for the first time series
    plt.figure(figsize=(16, 10))
    for i, strategy in enumerate(['linear', 'quadratic', 'cubic', 'nearest']):
        imputer = InterpolationImputer(strategy=strategy)
        X_imputed = imputer.transform(X)

        plt.subplot(2, 2, i + 1)
        plt.plot(X_imputed[0], 'o--', color='C1', label='Imputed')
        plt.plot(X[0], 'o--', color='C0', label='Original')
        plt.title("{0} Interpolation".format(strategy.capitalize()), fontsize=16)
        plt.legend(loc='best', fontsize=14)

    plt.suptitle('Interpolating missing values with different strategies',
                 fontsize=20)
    plt.tight_layout()
    plt.subplots_adjust(top=0.9)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.696 seconds)


.. _sphx_glr_download_auto_examples_preprocessing_plot_imputer.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_imputer.py <plot_imputer.py>`

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

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


.. only:: html

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

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