Skip to content
Permalink
main
Switch branches/tags
Go to file
* Debugging

* Remove the _Slice implementation

Instead of creating custom partition functions, use the already
implemented __iter__ functions and a common (optimized) partitioning
function. The problems with the old implementation was the
larger memory usage and a larger number of iterations through the data.

* Sort after grouping, which might improve grouping speed

* Remove the new unneeded class

* style formatting

* Changelog

* Dask Integration (#736)

* Dask integration, unfinished

* Fix test

* Added dask tests

* Improve the feature extraction test

* Reworked the documentation for the new features

* Changelog

* Stylefix

* Forget to add a class

* Increase test coverage

* Update feature_extraction_settings.rst (#740)

minimum/maximum are valid feature_calculators instead of min/max

https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html?highlight=extract_features#tsfresh.feature_extraction.feature_calculators.maximum

* Use a better download library (#741)

* Closes #743 (#744)

* Closes #743

* Adds issue (#743) info to changelog

* Fix the failure with the latest statsmodels installed (#749)

* limits lag length to 50% of sample size in `partial_autocorrelation`.

* try to fix ut

* fix ut

Co-authored-by: hekaisheng <kaisheng.hks@alibaba-inc.com>

* Fix #742, while taking into account the differences between Python's indexing of vectors and Matlab's indexing (cf. Bastia et al (2004), Eq. 1)

* Update docs/text/data_formats.rst

Co-authored-by: HaveF <iamaplayer@gmail.com>
Co-authored-by: patrjon <46594327+patrjon@users.noreply.github.com>
Co-authored-by: He Kaisheng <heks93@163.com>
Co-authored-by: hekaisheng <kaisheng.hks@alibaba-inc.com>
Co-authored-by: akem134@elan <a.kempa-liehr@auckland.ac.nz>

Co-authored-by: HaveF <iamaplayer@gmail.com>
Co-authored-by: patrjon <46594327+patrjon@users.noreply.github.com>
Co-authored-by: He Kaisheng <heks93@163.com>
Co-authored-by: hekaisheng <kaisheng.hks@alibaba-inc.com>
Co-authored-by: akem134@elan <a.kempa-liehr@auckland.ac.nz>
9 contributors

Users who have contributed to this file

@nils-braun @MaxBenChrist @kohlrabi90 @nikhase @jneuff @HaveF @hekaisheng @kempa-liehr @patrjon

some characteristics of the time series

tsfresh

This is the documentation of tsfresh.

tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.

You can jump right into the package by looking into our :ref:`quick-start-label`.

Contents

The following chapters will explain the tsfresh package in detail:

.. toctree::
   :maxdepth: 1

   text/introduction
   text/quick_start
   text/data_formats
   text/sklearn_transformers
   text/list_of_features
   text/feature_extraction_settings
   text/feature_filtering
   text/how_to_add_custom_feature
   text/large_data
   text/tsfresh_on_a_cluster
   text/forecasting
   text/faq
   api/modules
   authors
   license
   changes
   text/how_to_contribute
   text/feature_calculation


Indices and tables

Acknowledgements

The research and development of TSFRESH was funded in part by the German Federal Ministry of Education and Research under grant number 01IS14004 (project iPRODICT).