{ "info": { "author": "Pavel Senin", "author_email": "seninp@gmail.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Information Technology", "Operating System :: OS Independent", "Programming Language :: Python" ], "description": "Time series symbolic discretization with SAX\n=============================================\n\n.. image:: https://img.shields.io/pypi/v/saxpy.svg\n :target: https://pypi.python.org/pypi/saxpy\n :alt: Latest PyPI version\n\n.. image:: https://travis-ci.org/seninp/saxpy.png\n :target: https://travis-ci.org/seninp/saxpy\n :alt: Latest Travis CI build status\n\n.. image:: https://codecov.io/gh/seninp/saxpy/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/seninp/saxpy\n\n.. image:: http://img.shields.io/:license-gpl2-green.svg\n :target: http://www.gnu.org/licenses/gpl-2.0.html\n\n\nThis code is released under `GPL v.2.0 `_ and implements in Python:\n * Symbolic Aggregate approXimation (i.e., SAX) stack [LIN2002]\n * a simple function for time series motif discovery [PATEL2001]\n * HOT-SAX - a time series anomaly (discord) discovery algorithm [KEOGH2005]\n\n.. [LIN2002] Lin, J., Keogh, E., Patel, P., and Lonardi, S., `Finding Motifs in Time Series `_, The 2nd Workshop on Temporal Data Mining, the 8th ACM Int'l Conference on KDD (2002)\n.. [PATEL2001] Patel, P., Keogh, E., Lin, J., Lonardi, S., `Mining Motifs in Massive Time Series Databases `__, In Proc. ICDM (2002)\n.. [KEOGH2005] Keogh, E., Lin, J., Fu, A., `HOT SAX: Efficiently finding the most unusual time series subsequence `__, In Proc. ICDM (2005)\n\nNote that the most of the library's functionality is also available in `R `__ and `Java `__\n\n\nCiting this work:\n------------------\nIf you are using this implementation for you academic work, please cite our `Grammarviz 2.0\npaper `__:\n\n.. [SENIN2014] Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S., Lerner, M., `GrammarViz 2.0: a tool for grammar-based pattern discovery in time series `__, ECML/PKDD, 2014.\n\nIn a nutshell\n--------------\nSAX is used to transform a sequence of rational numbers (i.e., a time series) into a sequence of letters (i.e., a string) which is (typically) much shorterthan the input time series. Thus, SAX transform addresses a chief problem in time-series analysis -- the dimensionality curse.\n\nThis is an illustration of a time series of 128 points converted into the word of 8 letters:\n\n.. figure:: https://raw.githubusercontent.com/jMotif/SAX/master/src/resources/sax_transform.png\n :alt: SAX in a nutshell\n\n SAX in a nutshell\n\nAs discretization is probably the most used transformation in data\nmining, SAX has been widely used throughout the field. 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