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Packages","R","Related Software"],"sub_categories":["2020","Python","Time Series","Time-Series Analysis"],"readme":".. image:: https://matrixprofile.org/static/img/mpf-logo.png\n    :target: https://matrixprofile.org\n    :height: 300px\n    :scale: 50%\n    :alt: MPF Logo\n|\n|\n.. image:: https://img.shields.io/pypi/v/matrixprofile.svg\n    :target: https://pypi.org/project/matrixprofile/\n    :alt: PyPI Version\n.. image:: https://pepy.tech/badge/matrixprofile\n    :target: https://pepy.tech/project/matrixprofile\n    :alt: PyPI Downloads\n.. image:: https://img.shields.io/conda/vn/conda-forge/matrixprofile.svg\n    :target: https://anaconda.org/conda-forge/matrixprofile\n    :alt: Conda Version\n.. image:: https://img.shields.io/conda/dn/conda-forge/matrixprofile.svg\n    :target: https://anaconda.org/conda-forge/matrixprofile\n    :alt: Conda Downloads\n.. image:: https://codecov.io/gh/matrix-profile-foundation/matrixprofile/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/matrix-profile-foundation/matrixprofile\n    :alt: Code Coverage\n.. image:: https://dev.azure.com/conda-forge/feedstock-builds/_apis/build/status/matrixprofile-feedstock?branchName=master\n    :target: https://dev.azure.com/conda-forge/feedstock-builds/_build/latest?definitionId=11637\u0026branchName=master\n    :alt: Azure Pipelines\n.. image:: https://api.travis-ci.com/matrix-profile-foundation/matrixprofile.svg?branch=master\n    :target: https://travis-ci.com/matrix-profile-foundation/matrixprofile\n    :alt: Build Status\n.. image:: https://img.shields.io/conda/pn/conda-forge/matrixprofile.svg\n    :target: https://anaconda.org/conda-forge/matrixprofile\n    :alt: Platforms\n.. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg\n    :target: https://opensource.org/licenses/Apache-2.0\n    :alt: License\n.. image:: https://img.shields.io/twitter/follow/matrixprofile.svg?style=social\n    :target: https://twitter.com/matrixprofile\n    :alt: Twitter\n.. image:: https://img.shields.io/discord/589321741277462559?logo=discord\n    :target: https://discordapp.com/invite/sBhDNXT\n    :alt: Discord\n.. image:: https://joss.theoj.org/papers/10.21105/joss.02179/status.svg\n   :target: https://doi.org/10.21105/joss.02179\n   :alt: JOSSDOI\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3789780.svg\n   :target: https://doi.org/10.5281/zenodo.3789780\n   :alt: ZenodoDOI\n\nMatrixProfile\n----------------\nNOTE: THIS LIBRARY IS NOT ACTIVELY SUPPORTED. PLEASE CHECK OUT THE TD AMERITRADE STUMPY LIBRARY INSTEAD: https://github.com/TDAmeritrade/stumpyhttps://github.com/TDAmeritrade/stumpy\n\nMatrixProfile is a Python 3 library, brought to you by the `Matrix Profile Foundation \u003chttps://matrixprofile.org\u003e`_, for mining time series data. The Matrix Profile is a novel data structure with corresponding algorithms (stomp, regimes, motifs, etc.) developed by the `Keogh \u003chttps://www.cs.ucr.edu/~eamonn/MatrixProfile.html\u003e`_ and `Mueen \u003chttps://www.cs.unm.edu/~mueen/\u003e`_ 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.\n\nIn 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.\n\n* `tsmp \u003chttps://github.com/matrix-profile-foundation/tsmp\u003e`_ - an R implementation\n* `go-matrixprofile \u003chttps://github.com/matrix-profile-foundation/go-matrixprofile\u003e`_ - a Golang implementation\n\nPython Support\n----------------\nCurrently, we support the following versions of Python:\n\n* 3.5\n* 3.6\n* 3.7\n* 3.8\n* 3.9\n\nPython 2 is no longer supported. There are earlier versions of this library that support Python 2.\n\nInstallation\n------------\nThe 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 \u003chttp://matrixprofile.docs.matrixprofile.org/install.html\u003e`_ for your platform.\n\nInstallation with pip\n\n.. code-block:: bash\n\n   pip install matrixprofile\n\nInstallation with conda\n\n.. code-block:: bash\n\n   conda config --add channels conda-forge\n   conda install matrixprofile\n\nGetting Started\n---------------\nThis article provides introductory material on the Matrix Profile:\n`Introduction to Matrix Profiles  \u003chttps://towardsdatascience.com/introduction-to-matrix-profiles-5568f3375d90\u003e`_\n\n\nThis article provides details about core concepts introduced in this library:\n`How To Painlessly Analyze Your Time Series  \u003chttps://towardsdatascience.com/how-to-painlessly-analyze-your-time-series-f52dab7ea80d\u003e`_\n\nOur documentation provides a `quick start guide \u003chttp://matrixprofile.docs.matrixprofile.org/Quickstart.html\u003e`_, `examples \u003chttp://matrixprofile.docs.matrixprofile.org/examples.html\u003e`_ and `api \u003chttp://matrixprofile.docs.matrixprofile.org/api.html\u003e`_ documentation. It is the source of truth for getting up and running.\n\nAlgorithms\n----------\nFor details about the algorithms implemented, including performance characteristics, please refer to the `documentation \u003chttp://matrixprofile.docs.matrixprofile.org/Algorithms.html\u003e`_.\n            \n------------\nGetting Help\n------------\nWe provide a dedicated `Discord channel \u003chttps://discordapp.com/invite/sBhDNXT\u003e`_ 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 \u003chttps://github.com/matrix-profile-foundation/matrixprofile/issues\u003e`_.\n\n------------\nContributing\n------------\nPlease review the `contributing guidelines \u003chttp://matrixprofile.docs.matrixprofile.org/contributing.html\u003e`_ located in our documentation.\n\n---------------\nCode of Conduct\n---------------\nPlease review our `Code of Conduct documentation \u003chttp://matrixprofile.docs.matrixprofile.org/code_of_conduct.html\u003e`_.\n\n---------\nCitations\n---------\nAll proper acknowledgements for works of others may be found in our `citation documentation \u003chttp://matrixprofile.docs.matrixprofile.org/citations.html\u003e`_.\n\n------\nCiting\n------\nPlease cite this work using the `Journal of Open Source Software article \u003chttps://joss.theoj.org/papers/10.21105/joss.02179\u003e`_.\n\n    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\n\n.. code:: bibtex\n\n    @article{Van Benschoten2020,\n        doi = {10.21105/joss.02179},\n        url = {https://doi.org/10.21105/joss.02179},\n        year = {2020},\n        publisher = {The Open Journal},\n        volume = {5},\n        number = {49},\n        pages = {2179},\n        author = {Andrew Van Benschoten and Austin Ouyang and Francisco Bischoff and Tyler Marrs},\n        title = {MPA: a novel cross-language API for time series analysis},\n        journal = {Journal of Open Source Software}\n    }\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatrix-profile-foundation%2Fmatrixprofile","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatrix-profile-foundation%2Fmatrixprofile","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatrix-profile-foundation%2Fmatrixprofile/lists"}