=============================== MatrixProfile Documentation =============================== .. image:: https://img.shields.io/pypi/v/matrixprofile.svg :target: https://pypi.org/project/matrixprofile/ :alt: PyPI Version .. image:: https://pepy.tech/badge/matrixprofile :target: https://pepy.tech/project/matrixprofile :alt: PyPI Downloads .. image:: https://codecov.io/gh/matrix-profile-foundation/matrixprofile/branch/master/graph/badge.svg :target: https://codecov.io/gh/matrix-profile-foundation/matrixprofile :alt: Code Coverage .. image:: https://api.travis-ci.com/matrix-profile-foundation/matrixprofile.svg?branch=master :target: https://travis-ci.com/matrix-profile-foundation/matrixprofile :alt: Build Status .. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg :target: https://opensource.org/licenses/Apache-2.0 :alt: License .. image:: https://img.shields.io/twitter/follow/matrixprofile.svg?style=social :target: https://twitter.com/matrixprofile :alt: Twitter .. image:: https://img.shields.io/discord/589321741277462559?logo=discord :target: https://discordapp.com/invite/sBhDNXT :alt: Discord .. image:: https://joss.theoj.org/papers/10.21105/joss.02179/status.svg :target: https://doi.org/10.21105/joss.02179 :alt: JOSSDOI .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3789780.svg :target: https://doi.org/10.5281/zenodo.3789780 :alt: ZenodoDOI | .. image:: https://matrixprofile.org/static/img/mpf-logo.png :target: https://matrixprofile.org :height: 300px :scale: 50% :alt: MPF Logo MatrixProfile ---------------- 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. If you would like to install it from source, please review the `installation documentation `_ for your platform. .. code-block:: bash pip 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 `_. ------------ 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. --------------- Code of Conduct --------------- Please review our `Code of Conduct documentation `_. --------- 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} }

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