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https://github.com/matrix-profile-foundation/matrixprofile

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.
https://github.com/matrix-profile-foundation/matrixprofile

algorithms anomaly-detection clustering data-mining data-science hacktoberfest matrixprofile motif-discovery python python2 python3 segmentation time-series time-series-analysis

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A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

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README 

        
.. image:: https://matrixprofile.org/static/img/mpf-logo.png

    :target: https://matrixprofile.org

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    :scale: 50%

    :alt: MPF Logo

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    :alt: PyPI Version

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    :alt: Twitter

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MatrixProfile

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NOTE: THIS LIBRARY IS NOT ACTIVELY SUPPORTED. PLEASE CHECK OUT THE TD AMERITRADE STUMPY LIBRARY INSTEAD: https://github.com/TDAmeritrade/stumpyhttps://github.com/TDAmeritrade/stumpy



MatrixProfile is a Python 3 library, brought to you by the `Matrix Profile Foundation `_, for mining time series data. The Matrix Profile is a novel data structure with corresponding algorithms (stomp, regimes, motifs, etc.) developed by the `Keogh `_ and `Mueen `_ research groups at UC-Riverside and the University of New Mexico. The goal of this library is to make these algorithms accessible to both the novice and expert through standardization of core concepts, a simplistic API, and sensible default parameter values.



In addition to this Python library, the Matrix Profile Foundation, provides implementations in other languages. These languages have a pretty consistent API allowing you to easily switch between them without a huge learning curve.



* `tsmp `_ - an R implementation

* `go-matrixprofile `_ - a Golang implementation



Python Support

----------------

Currently, we support the following versions of Python:



* 3.5

* 3.6

* 3.7

* 3.8

* 3.9



Python 2 is no longer supported. There are earlier versions of this library that support Python 2.



Installation

------------

The easiest way to install this library is using pip or conda. If you would like to install it from source, please review the `installation documentation `_ for your platform.



Installation with pip



.. code-block:: bash



   pip install matrixprofile



Installation with conda



.. code-block:: bash



   conda config --add channels conda-forge

   conda install matrixprofile



Getting Started

---------------

This article provides introductory material on the Matrix Profile:

`Introduction to Matrix Profiles  `_



This article provides details about core concepts introduced in this library:

`How To Painlessly Analyze Your Time Series  `_



Our documentation provides a `quick start guide `_, `examples `_ and `api `_ documentation. It is the source of truth for getting up and running.



Algorithms

----------

For details about the algorithms implemented, including performance characteristics, please refer to the `documentation `_.

            

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Getting Help

------------

We provide a dedicated `Discord channel `_ where practitioners can discuss applications and ask questions about the Matrix Profile Foundation libraries. If you rather not join Discord, then please open a `Github issue `_.



------------

Contributing

------------

Please review the `contributing guidelines `_ located in our documentation.



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Code of Conduct

---------------

Please review our `Code of Conduct documentation `_.



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Citations

---------

All proper acknowledgements for works of others may be found in our `citation documentation `_.



------

Citing

------

Please cite this work using the `Journal of Open Source Software article `_.



    Van Benschoten et al., (2020). MPA: a novel cross-language API for time series analysis. Journal of Open Source Software, 5(49), 2179, https://doi.org/10.21105/joss.02179



.. code:: bibtex



    @article{Van Benschoten2020,

        doi = {10.21105/joss.02179},

        url = {https://doi.org/10.21105/joss.02179},

        year = {2020},

        publisher = {The Open Journal},

        volume = {5},

        number = {49},

        pages = {2179},

        author = {Andrew Van Benschoten and Austin Ouyang and Francisco Bischoff and Tyler Marrs},

        title = {MPA: a novel cross-language API for time series analysis},

        journal = {Journal of Open Source Software}

    }