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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_forecast_plot_holt_winters_predictor.py:


================================
Holt-Winters Predictor
================================

For the Holt-Winters example we use forged daily data with a weekly seasonality. We predict two types of data, the first
dataset displays an additive trend and an additive seasonality, and the second dataset displays an additive trend and a
multiplicative seasonality.

.. GENERATED FROM PYTHON SOURCE LINES 12-50



.. image-sg:: /auto_examples/forecast/images/sphx_glr_plot_holt_winters_predictor_001.png
   :alt: Forecast for data with weekly seasonality and additive trend, Forecast for data with weekly seasonality, additive trend, and multiplicative seasonality
   :srcset: /auto_examples/forecast/images/sphx_glr_plot_holt_winters_predictor_001.png
   :class: sphx-glr-single-img





.. code-block:: default

    import os
    import warnings

    import matplotlib.pyplot as plt
    import pandas as pd

    from indsl.forecast.holt_winters_predictor import holt_winters_predictor as hwp


    # suppress "No frequency information was given" warning - Frequency information is derived from datetime index
    warnings.filterwarnings("ignore")

    base_path = "" if __name__ == "__main__" else os.path.dirname(__file__)
    data = pd.read_csv(os.path.join(base_path, "../../datasets/data/seasonal_with_trend_data.csv"), sep=";", index_col=0)
    data.index = pd.to_datetime(data.index)

    # calculate the forecast for both data types
    additive_res = hwp(data["additive"], seasonal_periods=7, steps=90)
    multiplicative_res = hwp(data["multiplicative"], seasonal_periods=7, seasonality="mul", steps=90)

    # plot result
    fig, ax = plt.subplots(2, 1, figsize=[9, 7])
    ax[0].plot(data.index, data["additive"], label="Train")
    ax[0].plot(additive_res.index, additive_res, label="Holt-Winters")
    ax[0].set_ylabel("Value")
    ax[0].set_title("Forecast for data with weekly seasonality and additive trend")

    ax[1].plot(data.index, data["multiplicative"], label="Train")
    ax[1].plot(multiplicative_res.index, multiplicative_res, label="Holt-Winters")
    ax[1].set_title("Forecast for data with weekly seasonality, additive trend, and multiplicative seasonality")
    ax[1].set_ylabel("Value")

    _ = ax[0].legend(loc=0)
    _ = ax[1].legend(loc=0)

    fig.tight_layout(pad=2.0)

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.442 seconds)


.. _sphx_glr_download_auto_examples_forecast_plot_holt_winters_predictor.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_holt_winters_predictor.py <plot_holt_winters_predictor.py>`



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

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


.. only:: html

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

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