Metadata-Version: 1.1
Name: tsfresh
Version: 0.3.0
Summary: tsfresh extracts relevant characteristics from time series
Home-page: https://github.com/blue-yonder/tsfresh
Author: Maximilian Christ, Nils Braun, Julius Neuffer
Author-email: max.christ@me.com
License: MIT
Description: |Documentation Status| |Build Status| |Coverage Status| |license|
        |Gitter chat| |py27 status| |py352 status|
        
        tsfresh
        =======
        
        This repository contains the *TSFRESH* python package. The abbreviation
        stands for
        
        *"Time Series Feature extraction based on scalable hypothesis tests"*.
        
        The package contains many feature extraction methods and a robust
        feature selection algorithm.
        
        Spend less time on feature engineering
        --------------------------------------
        
        Data Scientists often spend most of their time either cleaning data or
        building features. While we cannot change the first thing, the second
        can be automated. *TSFRESH* frees your time spend on building features
        by extracting them automatically. Hence, you have more time to study the
        newest deep learning paper, read hacker news or build better models.
        
        Automatic extraction of 100s of features
        ----------------------------------------
        
        *TSFRESH* automatically extracts 100s of features from time series.
        Those 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.
        
        .. figure:: docs/images/introduction_ts_exa_features.png
           :alt: The features extracted from a exemplary time series
        
           The features extracted from a exemplary time series
        
        The 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 classification tasks.
        
        Forget irrelevant features
        --------------------------
        
        Time series often contain noise, redundancies or irrelevant information.
        As a result most of the extracted features will not be useful for the
        machine learning task at hand.
        
        To avoid extracting irrelevant features, the *TSFRESH* package has a
        built-in filtering procedure. This filtering procedure evaluates the
        explaining power and importance of each characteristic for the
        regression or classification tasks at hand.
        
        It is based on the well developed theory of hypothesis testing and uses
        a multiple test procedure. As a result the filtering process
        mathematically controls the percentage of irrelevant extracted features.
        
        The algorithm is described in the following paper
        
        -  Christ, M., Kempa-Liehr, A.W. and Feindt, M. (2016).
           *Distributed and parallel time series feature extraction for
           industrial big data applications.*
           ArXiv e-print 1610.07717, https://arxiv.org/abs/1610.07717.
        
        Advantages of tsfresh
        ---------------------
        
        *TSFRESH* has several selling points, for example
        
        1. it is field tested
        2. it is unit tested
        3. the filtering process is statistically/mathematically correct
        4. it has a comprehensive documentation
        5. it is compatible with sklearn, pandas and numpy
        6. it allows anyone to easily add their favorite features
        
        Next steps
        ----------
        
        If you are interested in the technical workings, go to see our
        comprehensive Read-The-Docs documentation at
        http://tsfresh.readthedocs.io.
        
        The algorithm, especially the filtering part are also described in the
        paper mentioned above.
        
        If 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>`__
        
        .. |Documentation Status| image:: https://readthedocs.org/projects/tsfresh/badge/?version=latest
           :target: http://tsfresh.readthedocs.io/en/latest/?badge=latest
        .. |Build Status| image:: https://travis-ci.org/blue-yonder/tsfresh.svg?branch=master
           :target: https://travis-ci.org/blue-yonder/tsfresh
        .. |Coverage Status| image:: https://coveralls.io/repos/github/blue-yonder/tsfresh/badge.svg?branch=master
           :target: https://coveralls.io/github/blue-yonder/tsfresh?branch=master
        .. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg
           :target: https://github.com/blue-yonder/tsfresh/blob/master/LICENSE.txt
        .. |Gitter chat| image:: https://badges.gitter.im/tsfresh/Lobby.svg
           :target: https://gitter.im/tsfresh/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link
        .. |py27 status| image:: https://img.shields.io/badge/python2.7-supported-green.svg
        .. |py352 status| image:: https://img.shields.io/badge/python3.5.2-supported-green.svg
           :target: https://github.com/blue-yonder/tsfresh/issues/8
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Operating System :: Unix
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
