.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/statistics/plot_detect_outliers_001.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_statistics_plot_detect_outliers_001.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_statistics_plot_detect_outliers_001.py:


=======================================================
Outlier detection with DBSCAN and spline regression 001
=======================================================

Example of outlier detection in a randomly generated time series data using DBSCAN and spline regression.
The resulting figure shows outlier indicator time series generated with a time window of 60min plotted on the original
time series.

.. GENERATED FROM PYTHON SOURCE LINES 11-61



.. image-sg:: /auto_examples/statistics/images/sphx_glr_plot_detect_outliers_001_001.png
   :alt: Outlier identification for a time series for a duration of 60 minutes
   :srcset: /auto_examples/statistics/images/sphx_glr_plot_detect_outliers_001_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    from datetime import datetime, timedelta

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    from indsl.statistics.outliers import detect_outliers


    # Generate time series with outliers
    rng1 = np.random.default_rng(0)

    mu = 0
    sigma = 1
    outliers_positive = rng1.uniform(low=3 * sigma, high=5 * sigma, size=2)
    outliers_negative = rng1.uniform(low=-5 * sigma, high=-3 * sigma, size=2)
    values = np.concatenate((outliers_positive, outliers_negative, rng1.normal(mu, sigma, 240)), axis=0)

    rng1.shuffle(values)

    data = pd.Series(values, index=pd.date_range("2021-02-09 00:00:00", "2021-03-01 09:00:00", periods=244))


    # Plot outliers against actual data
    fig, ax1 = plt.subplots(figsize=(15, 5))

    # Plot actual time series data
    ax1.plot(data.index, data, label="Time series", marker=".", color="blue")

    ts_values = np.arange(data.index[0], data.index[-1], timedelta(days=1)).astype(datetime)

    ax1.set_xticks(ts_values)
    ax1.set_xticklabels([ts.strftime("%d-%m-%Y \n %H:%M:%S") for ts in ts_values], fontsize=8)

    # Plot outliers indicator time series
    ax2 = ax1.twinx()
    ax2.plot(detect_outliers(data).index, detect_outliers(data).values, label="Outlier indicator", marker=".", color="red")

    # Place legend
    ax1.legend(loc="upper left")
    ax2.legend(loc="upper right")

    plt.xlabel("Timestamp")
    ax1.set_ylabel("Time series values")
    ax2.set_ylabel("Outlier indicator")

    fig.suptitle("Outlier identification for a time series for a duration of 60 minutes", fontsize=14)
    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  32.321 seconds)


.. _sphx_glr_download_auto_examples_statistics_plot_detect_outliers_001.py:


.. only :: html

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



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

     :download:`Download Python source code: plot_detect_outliers_001.py <plot_detect_outliers_001.py>`



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

     :download:`Download Jupyter notebook: plot_detect_outliers_001.ipynb <plot_detect_outliers_001.ipynb>`


.. only:: html

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

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