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        "author": "Maximilian Christ, Nils Braun, Julius Neuffer",
        "author_email": "max.christ@me.com",
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            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
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            "Topic :: Scientific/Engineering",
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        "description": "[![Documentation Status](https://readthedocs.org/projects/tsfresh/badge/?version=latest)](https://tsfresh.readthedocs.io/en/latest/?badge=latest)\n[![Build Status](https://travis-ci.org/blue-yonder/tsfresh.svg?branch=master)](https://travis-ci.org/blue-yonder/tsfresh)\n[![Coverage Status](https://coveralls.io/repos/github/blue-yonder/tsfresh/badge.svg?branch=master)](https://coveralls.io/github/blue-yonder/tsfresh?branch=master)\n[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/blue-yonder/tsfresh/blob/master/LICENSE.txt)\n[![Gitter chat](https://badges.gitter.im/tsfresh/Lobby.svg)](https://gitter.im/tsfresh/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link)\n![py27 status](https://img.shields.io/badge/python2.7-supported-green.svg)\n[![py352 status](https://img.shields.io/badge/python3.5.2-supported-green.svg)](https://github.com/blue-yonder/tsfresh/issues/8)\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/blue-yonder/tsfresh/master?filepath=notebooks)\n[![Downloads](https://pepy.tech/badge/tsfresh)](https://pepy.tech/project/tsfresh)\n\n\n# tsfresh\n\nThis repository contains the *TSFRESH* python package. The abbreviation stands for\n\n*\"Time Series Feature extraction based on scalable hypothesis tests\"*.\n\nThe package contains many feature extraction methods and a robust feature selection algorithm.\n\n## Spend less time on feature engineering\n\nData Scientists often spend most of their time either cleaning data or building features.\nWhile we cannot change the first thing, the second can be automated.\n*TSFRESH* frees your time spent on building features by extracting them automatically.\nHence, you have more time to study the newest deep learning paper, read hacker news or build better models.\n\n\n## Automatic extraction of 100s of features\n\n*TSFRESH* automatically extracts 100s of features from time series.\nThose features describe basic characteristics of the time series such as the number of peaks, the average or maximal value or more complex features such as the time reversal symmetry statistic.\n\n![The features extracted from a exemplary time series](docs/images/introduction_ts_exa_features.png)\n\nThe set of features can then be used to construct statistical or machine learning models on the time series to be used for example in regression or\nclassification tasks.\n\n## Forget irrelevant features\n\nTime series often contain noise, redundancies or irrelevant information.\nAs a result most of the extracted features will not be useful for the machine learning task at hand.\n\nTo avoid extracting irrelevant features, the *TSFRESH* package has a built-in filtering procedure.\nThis filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classification tasks at hand.\n\nIt is based on the well developed theory of hypothesis testing and uses a multiple test procedure.\nAs a result the filtering process mathematically controls the percentage of irrelevant extracted features.\n\nThe  *TSFRESH* package is described in the following open access paper\n\n* Christ, M., Braun, N., Neuffer, J. and Kempa-Liehr A.W. (2018).\n   _Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh -- A Python package)._\n   Neurocomputing 307 (2018) 72-77, [doi:10.1016/j.neucom.2018.03.067](https://doi.org/10.1016/j.neucom.2018.03.067).\n\nThe FRESH algorithm is described in the following whitepaper\n\n* Christ, M., Kempa-Liehr, A.W. and Feindt, M. (2017).  \n    _Distributed and parallel time series feature extraction for industrial big data applications._  \n    ArXiv e-print 1610.07717,  [https://arxiv.org/abs/1610.07717](https://arxiv.org/abs/1610.07717).\n\n\n## Advantages of tsfresh\n\n*TSFRESH* has several selling points, for example\n\n1. it is field tested\n2. it is unit tested\n3. the filtering process is statistically/mathematically correct\n4. it has a comprehensive documentation\n5. it is compatible with sklearn, pandas and numpy\n6. it allows anyone to easily add their favorite features\n7. it both runs on your local machine or even on a cluster\n\n## Next steps\n\nIf you are interested in the technical workings, go to see our comprehensive Read-The-Docs documentation at [http://tsfresh.readthedocs.io](http://tsfresh.readthedocs.io).\n\nThe algorithm, especially the filtering part are also described in the paper mentioned above.\n\nIf you have some questions or feedback you can find the developers in the [gitter chatroom.](https://gitter.im/tsfresh/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link)\n\nWe appreciate any contributions, if you are interested in helping us to make *TSFRESH* the biggest archive of feature extraction methods in python, just head over to our [How-To-Contribute](http://tsfresh.readthedocs.io/en/latest/text/how_to_contribute.html) instructions.\n\n## Acknowledgements\n\nThe research and development of *TSFRESH* was funded in part by the German Federal Ministry of Education and Research under grant number 01IS14004 (project iPRODICT).\n\n\n",
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