:orphan:

.. _general_examples:

Introductory examples
---------------------

Introductory examples for time series in general.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Visualizing data is important and should usually be the first step in any analysis. This simple...">

.. only:: html

  .. image:: /auto_examples/images/thumb/sphx_glr_plot_ts_thumb.png
    :alt: Plotting a time series

  :ref:`sphx_glr_auto_examples_plot_ts.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Plotting a time series</div>
    </div>


.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/plot_ts


Approximating time series
-------------------------

Approximation algorithms try to capture the most important information
from time series. They can be seen as simple feature extraction algorithms.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Discrete Fourier Transform is a signal processing technique that transforms a signal of size n ...">

.. only:: html

  .. image:: /auto_examples/approximation/images/thumb/sphx_glr_plot_dft_thumb.png
    :alt: Discrete Fourier Transform

  :ref:`sphx_glr_auto_examples_approximation_plot_dft.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Discrete Fourier Transform</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Binning continuous data into intervals can be seen as an approximation that reduces noise and c...">

.. only:: html

  .. image:: /auto_examples/approximation/images/thumb/sphx_glr_plot_mcb_thumb.png
    :alt: Multiple Coefficient Binning

  :ref:`sphx_glr_auto_examples_approximation_plot_mcb.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Multiple Coefficient Binning</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Time series with a high sampling rate can be very noisy. In order to reduce noise, a technique ...">

.. only:: html

  .. image:: /auto_examples/approximation/images/thumb/sphx_glr_plot_paa_thumb.png
    :alt: Piecewise Aggregate Approximation

  :ref:`sphx_glr_auto_examples_approximation_plot_paa.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Piecewise Aggregate Approximation</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Binning continuous data into intervals can be seen as an approximation that reduces noise and c...">

.. only:: html

  .. image:: /auto_examples/approximation/images/thumb/sphx_glr_plot_sax_thumb.png
    :alt: Symbolic Aggregate approXimation

  :ref:`sphx_glr_auto_examples_approximation_plot_sax.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Symbolic Aggregate approXimation</div>
    </div>


.. raw:: html

    </div>


Bag-of-words transformation
---------------------------

Bag-of-words algorithms transform a sequence of symbols into a bag of words.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Time series are often transformed into sequences of symbols. Bag-of-words approaches are then u...">

.. only:: html

  .. image:: /auto_examples/bag_of_words/images/thumb/sphx_glr_plot_bow_thumb.png
    :alt: Bag of Words

  :ref:`sphx_glr_auto_examples_bag_of_words_plot_bow.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Bag of Words</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Time series are often transformed into sequences of symbols. Bag-of-words approaches are then u...">

.. only:: html

  .. image:: /auto_examples/bag_of_words/images/thumb/sphx_glr_plot_word_extractor_thumb.png
    :alt: Word Extractor

  :ref:`sphx_glr_auto_examples_bag_of_words_plot_word_extractor.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Word Extractor</div>
    </div>


.. raw:: html

    </div>


Classification algorithms
-------------------------

Classification algorithms can directly classify raw time series.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how the BOSSVS algorithm transforms a dataset consisting of time series and ...">

.. only:: html

  .. image:: /auto_examples/classification/images/thumb/sphx_glr_plot_bossvs_thumb.png
    :alt: Bag-of-SFA Symbols in Vector Space (BOSSVS)

  :ref:`sphx_glr_auto_examples_classification_plot_bossvs.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Bag-of-SFA Symbols in Vector Space (BOSSVS)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates what the LearningShapelets algorithm learns in the training phase. A s...">

.. only:: html

  .. image:: /auto_examples/classification/images/thumb/sphx_glr_plot_learning_shapelets_thumb.png
    :alt: Learning Time-Series Shapelets

  :ref:`sphx_glr_auto_examples_classification_plot_learning_shapelets.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Learning Time-Series Shapelets</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how the SAX-VSM algorithm transforms a dataset consisting of time series and...">

.. only:: html

  .. image:: /auto_examples/classification/images/thumb/sphx_glr_plot_saxvsm_thumb.png
    :alt: Symbolic Aggregate approXimation in Vector Space Model (SAX-VSM)

  :ref:`sphx_glr_auto_examples_classification_plot_saxvsm.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Symbolic Aggregate approXimation in Vector Space Model (SAX-VSM)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the feature importance scores of the final random forest classifier of...">

.. only:: html

  .. image:: /auto_examples/classification/images/thumb/sphx_glr_plot_tsbf_thumb.png
    :alt: Time Series Bag-of-Features

  :ref:`sphx_glr_auto_examples_classification_plot_tsbf.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Time Series Bag-of-Features</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates which information is considered important by the algorithm in order to...">

.. only:: html

  .. image:: /auto_examples/classification/images/thumb/sphx_glr_plot_time_series_forest_thumb.png
    :alt: Time Series Forest

  :ref:`sphx_glr_auto_examples_classification_plot_time_series_forest.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Time Series Forest</div>
    </div>


.. raw:: html

    </div>


Clustering time series
-------------------------

Depending on a suitable metric, it is possible to cluster time series.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows the differences between various metrics related to time series clustering. B...">

.. only:: html

  .. image:: /auto_examples/clustering/images/thumb/sphx_glr_plot_dtw_boss_thumb.png
    :alt: Time Series Clustering with DTW and BOSS

  :ref:`sphx_glr_auto_examples_clustering_plot_dtw_boss.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Time Series Clustering with DTW and BOSS</div>
    </div>


.. raw:: html

    </div>


Dataset utilities
-----------------

Examples on how to load and make time series datasets.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to load and plot the GunPoint dataset. This dataset involves one female ...">

.. only:: html

  .. image:: /auto_examples/datasets/images/thumb/sphx_glr_plot_load_gunpoint_thumb.png
    :alt: Loading the GunPoint dataset

  :ref:`sphx_glr_auto_examples_datasets_plot_load_gunpoint.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Loading the GunPoint dataset</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to generate a Cylinder-Bell-Funnel dataset. This simulated dataset was i...">

.. only:: html

  .. image:: /auto_examples/datasets/images/thumb/sphx_glr_plot_make_cbf_thumb.png
    :alt: Making a Cylinder-Bell-Funnel dataset

  :ref:`sphx_glr_auto_examples_datasets_plot_make_cbf.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Making a Cylinder-Bell-Funnel dataset</div>
    </div>


.. raw:: html

    </div>


Decomposing time series
-----------------------

Decomposition algorithms decompose time series into several components.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Singular Spectrum Analysis">

.. only:: html

  .. image:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_ssa_thumb.png
    :alt: Singular Spectrum Analysis

  :ref:`sphx_glr_auto_examples_decomposition_plot_ssa.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Singular Spectrum Analysis</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Trend-Seasonal decomposition with Singular Spectrum Analysis">

.. only:: html

  .. image:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_auto_ssa_thumb.png
    :alt: Trend-Seasonal decomposition with Singular Spectrum Analysis

  :ref:`sphx_glr_auto_examples_decomposition_plot_auto_ssa.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Trend-Seasonal decomposition with Singular Spectrum Analysis</div>
    </div>


.. raw:: html

    </div>


Imaging time series
-------------------

Imaging algorithms transform time series into images.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A Gramian angular field is an image obtained from a time series, representing some kind of temp...">

.. only:: html

  .. image:: /auto_examples/image/images/thumb/sphx_glr_plot_dataset_gaf_thumb.png
    :alt: Data set of Gramian angular fields

  :ref:`sphx_glr_auto_examples_image_plot_dataset_gaf.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data set of Gramian angular fields</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A Markov transition field is an image obtained from a time series, representing a field of tran...">

.. only:: html

  .. image:: /auto_examples/image/images/thumb/sphx_glr_plot_dataset_mtf_thumb.png
    :alt: Data set of Markov transition fields

  :ref:`sphx_glr_auto_examples_image_plot_dataset_mtf.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data set of Markov transition fields</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A recurrence plot is an image obtained from a time series, representing the pairwise Euclidean ...">

.. only:: html

  .. image:: /auto_examples/image/images/thumb/sphx_glr_plot_dataset_rp_thumb.png
    :alt: Data set of recurrence plots

  :ref:`sphx_glr_auto_examples_image_plot_dataset_rp.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data set of recurrence plots</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A Gramian angular field is an image obtained from a time series, representing some kind of temp...">

.. only:: html

  .. image:: /auto_examples/image/images/thumb/sphx_glr_plot_single_gaf_thumb.png
    :alt: Single Gramian angular field

  :ref:`sphx_glr_auto_examples_image_plot_single_gaf.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Single Gramian angular field</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A Markov transition field is an image obtained from a time series, representing a field of tran...">

.. only:: html

  .. image:: /auto_examples/image/images/thumb/sphx_glr_plot_single_mtf_thumb.png
    :alt: Single Markov transition field

  :ref:`sphx_glr_auto_examples_image_plot_single_mtf.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Single Markov transition field</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A recurrence plot is an image obtained from a time series, representing the pairwise Euclidean ...">

.. only:: html

  .. image:: /auto_examples/image/images/thumb/sphx_glr_plot_single_rp_thumb.png
    :alt: Single recurrence plot

  :ref:`sphx_glr_auto_examples_image_plot_single_rp.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Single recurrence plot</div>
    </div>


.. raw:: html

    </div>


Metrics
-------

Specific metrics for time series have been developed. The examples below
illustrate some of the implemented metrics.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to compute and visualize the optimal path when computing Dynamic Time Wa...">

.. only:: html

  .. image:: /auto_examples/metrics/images/thumb/sphx_glr_plot_dtw_thumb.png
    :alt: Dynamic Time Warping

  :ref:`sphx_glr_auto_examples_metrics_plot_dtw.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Dynamic Time Warping</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example explains how to set the max_slope parameter of the itakura parallelogram when comp...">

.. only:: html

  .. image:: /auto_examples/metrics/images/thumb/sphx_glr_plot_itakura_thumb.png
    :alt: Itakura parallelogram

  :ref:`sphx_glr_auto_examples_metrics_plot_itakura.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Itakura parallelogram</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example explains how to set the window_size parameter of the Sakoe-Chiba band when computi...">

.. only:: html

  .. image:: /auto_examples/metrics/images/thumb/sphx_glr_plot_sakoe_chiba_thumb.png
    :alt: Sakoe-Chiba band

  :ref:`sphx_glr_auto_examples_metrics_plot_sakoe_chiba.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Sakoe-Chiba band</div>
    </div>


.. raw:: html

    </div>


Multivariate time series
------------------------

Specific algorithms for multivariate time series have been developed.
The examples below illustrate some of the implemented ones.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A joint recurrence plot is an extension of recurrence plots ( implemented as pyts.image.Recurre...">

.. only:: html

  .. image:: /auto_examples/multivariate/images/thumb/sphx_glr_plot_joint_rp_thumb.png
    :alt: Joint Recurrence Plot

  :ref:`sphx_glr_auto_examples_multivariate_plot_joint_rp.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Joint Recurrence Plot</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="WEASELMUSE stand for Word ExtrAction for time SEries cLassification plus Multivariate Unsupervi...">

.. only:: html

  .. image:: /auto_examples/multivariate/images/thumb/sphx_glr_plot_weasel_muse_thumb.png
    :alt: WEASEL+MUSE

  :ref:`sphx_glr_auto_examples_multivariate_plot_weasel_muse.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">WEASEL+MUSE</div>
    </div>


.. raw:: html

    </div>


Preprocessing tools
-------------------

Preprocessing data is a common task in machine learning. The examples below
illustrate the preprocessing tools available in this module.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Missing values are common in real-word datasets and most algorithms cannot deal with them. Thus...">

.. only:: html

  .. image:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_imputer_thumb.png
    :alt: Imputer

  :ref:`sphx_glr_auto_examples_preprocessing_plot_imputer.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Imputer</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Scaling data is a usual requirement for many algorithms and allows to have identical scales for...">

.. only:: html

  .. image:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_scalers_thumb.png
    :alt: Scalers

  :ref:`sphx_glr_auto_examples_preprocessing_plot_scalers.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Scalers</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Some algorithms make assumptions on the distribution of the data. Therefore it can be useful to...">

.. only:: html

  .. image:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_transformers_thumb.png
    :alt: Transformers

  :ref:`sphx_glr_auto_examples_preprocessing_plot_transformers.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Transformers</div>
    </div>


.. raw:: html

    </div>


Transformation algorithms
-------------------------

Transformation algorithms try to capture the most important information
from time series using advanced transformation. They can be seen as complex
feature extraction algorithms.



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Bag-of-words approaches are common in time series classification. The Bag-of-patterns algorithm...">

.. only:: html

  .. image:: /auto_examples/transformation/images/thumb/sphx_glr_plot_bop_thumb.png
    :alt: Bag of Patterns

  :ref:`sphx_glr_auto_examples_transformation_plot_bop.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Bag of Patterns</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Bag-of-words approaches are common in time series classification. The Bag-of-SFA Symbols (BOSS)...">

.. only:: html

  .. image:: /auto_examples/transformation/images/thumb/sphx_glr_plot_boss_thumb.png
    :alt: Bag-of-SFA Symbols (BOSS)

  :ref:`sphx_glr_auto_examples_transformation_plot_boss.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Bag-of-SFA Symbols (BOSS)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The RandOm Convolutional KErnel Transform (ROCKET) algorithm randomly generates a great variety...">

.. only:: html

  .. image:: /auto_examples/transformation/images/thumb/sphx_glr_plot_rocket_thumb.png
    :alt: RandOm Convolutional KErnel Transform (ROCKET)

  :ref:`sphx_glr_auto_examples_transformation_plot_rocket.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">RandOm Convolutional KErnel Transform (ROCKET)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The Shapelet Transform algorithm extracts shapelets from a data set of time series and returns ...">

.. only:: html

  .. image:: /auto_examples/transformation/images/thumb/sphx_glr_plot_shapelet_transform_thumb.png
    :alt: Shapelet Transform

  :ref:`sphx_glr_auto_examples_transformation_plot_shapelet_transform.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Shapelet Transform</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Bag-of-words approaches are common in time series classification. The Word ExtrAction for time ...">

.. only:: html

  .. image:: /auto_examples/transformation/images/thumb/sphx_glr_plot_weasel_thumb.png
    :alt: Word ExtrAction for time SEries cLassification (WEASEL)

  :ref:`sphx_glr_auto_examples_transformation_plot_weasel.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Word ExtrAction for time SEries cLassification (WEASEL)</div>
    </div>


.. raw:: html

    </div>


.. toctree::
   :hidden:
   :includehidden:

   /auto_examples/approximation/index.rst
   /auto_examples/bag_of_words/index.rst
   /auto_examples/classification/index.rst
   /auto_examples/clustering/index.rst
   /auto_examples/datasets/index.rst
   /auto_examples/decomposition/index.rst
   /auto_examples/image/index.rst
   /auto_examples/metrics/index.rst
   /auto_examples/multivariate/index.rst
   /auto_examples/preprocessing/index.rst
   /auto_examples/transformation/index.rst


.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-gallery

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

      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

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

      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


.. only:: html

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

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