Changelog

tsfresh uses Semantic Versioning

Version 0.20.2

  • Added Features

    • Make Dask and Distributed optional dependencies (#1061)

    • View and Set N Jobs (#1029)

  • Bugfixes/Typos/Documentation:

    • Extra notes on parallelization efficiencies (#1046)

    • Update doc extraction settings for clarity and formatting (#1033)

    • Typos (#1031, #1034, #1049, #1048)

Version 0.20.1

  • Added Features

    • Make tsfresh compatible with numpy 1.24 (#1018) and pandas 2.0 (#1028)

  • Bugfixes/Typos/Documentation:

    • Use pandas Index.equals in check_if_pandas_series (#963)

    • Updates to package layout, CI/CD and developer setup

Version 0.20.0

  • Breaking Change

    • The matrixprofile package becomes an optional dependency

  • Bugfixes/Typos/Documentation:

    • Fix feature extraction of Friedrich coefficients for pandas>1.3.5

    • Fix file paths after example notebooks were moved

Version 0.19.0

  • Breaking Change

    • Drop Python 3.6 support due to dependency on statsmodels 0.13

  • Added Features

    • Improve documentation (#831, #834, #851, #853, #870)

    • Add absolute_maximum and mean_n_absolute_max features (#833)

    • Make settings pickable (#845, #847, #910)

    • Disable multiprocessing for n_jobs=1 (#852)

    • Add black, isort, and pre-commit (#876)

  • Bugfixes/Typos/Documentation:

    • Fix conversion of time-series into sequence for lempel_ziv_complexity (#806)

    • Fix range count config (#827)

    • Reword documentation (#893)

    • Fix statsmodels deprecation issues (#898, #912)

    • Fix typo in requirements (#903)

    • Bump statsmodels to v0.13 (#

    • Updated references

Version 0.18.0

  • Added Features

    • Allow arbitrary rolling sizes (#766)

    • Allow for multiclass significance tests (#762)

    • Add multiclass option to RelevantFeatureAugmenter (#782)

    • Addition of matrix_profile feature (#793)

    • Added new query similarity counter feature (#798)

    • Add root mean square feature (#813)

  • Bugfixes/Typos/Documentation:

    • Do not send coverage of notebook tests to codecov (#759)

    • Fix typos in notebook (#757, #780)

    • Fix output format of make_forecasting_frame (#758)

    • Fix badges and remove benchmark test

    • Fix BY notebook plot (#760)

    • Ts forecast example improvement (#763)

    • Also surpress warnings in dask (#769)

    • Update relevant_feature_augmenter.py (#779)

    • Fix column names in quick_start.rst (#778)

    • Improve relevance table function documentation (#781)

    • Fixed #789 Typo in “how to add custom feature” (#790)

    • Convert to the correct type on warnings (#799)

    • Fix minor typos in the docs (#802)

    • Add unwanted filetypes to gitignore (#819)

    • Fix build and test failures (#815)

    • Fix imputing docu (#800)

    • Bump the scikit-learn version (#822)

Version 0.17.0

We changed the default branch from “master” to “main”.

  • Breaking Change

    • Changed constructed id in roll_time_series from string to tuple (#700)

    • Same for add_sub_time_series_index (#720)

  • Added Features

    • Implemented the Lempel-Ziv-Complexity and the Fourier Entropy (#688)

    • Prevent #524 by adding an assert for common identifiers (#690)

    • Added permutation entropy (#691)

    • Added a logo :-) (#694)

    • Implemented the benford distribution feature (#689)

    • Reworked the notebooks (#701, #704)

    • Speed up the result pivoting (#705)

    • Add a test for the dask bindings (#719)

    • Refactor input data iteration to need less memory (#707)

    • Added benchmark tests (#710)

    • Make dask a possible input format (#736)

  • Bugfixes:

    • Fixed a bug in the selection, that caused all regression tasks with un-ordered index to be wrong (#715)

    • Fixed readthedocs (#695, #696)

    • Fix spark and dask after #705 and for non-id named id columns (#712)

    • Fix in the forecasting notebook (#729)

    • Let tsfresh choose the value column if possible (#722)

    • Move from coveralls github action to codecov (#734)

    • Improve speed of data processing (#735)

    • Fix for newer, more strict pandas versions (#737)

    • Fix documentation for feature calculators (#743)

Version 0.16.0

  • Breaking Change

    • Fix the sorting of the parameters in the feature names (#656) The feature names consist of a sorted list of all parameters now. That used to be true for all non-combiner features, and is now also true for combiner features. If you relied on the actual feature name, this is a breaking change.

    • Change the id after the rolling (#668) Now, the old id of your data is still kept. Additionally, we improved the way dataframes without a time column are rolled and how the new sub-time series are named. Also, the documentation was improved a lot.

  • Added Features

    • Added variation coefficient (#654)

    • Added the datetimeindex explanation from the notebook to the docs (#661)

    • Optimize RelevantFeatureAugmenter to avoid re-extraction (#669)

    • Added a function add_sub_time_series_index (#666)

    • Added Dockerfile

    • Speed optimizations and speed testing script (#681)

  • Bugfixes

    • Increase the extracted ar coefficients to the full parameter range. (#662)

    • Documentation fixes (#663, #664, #665)

    • Rewrote the sample_entropy feature calculator (#681) It is now faster and (hopefully) more correct. But your results will change!

Version 0.15.1

  • Changelog and documentation fixes

Version 0.15.0

  • Added Features

    • Add count_above and count_below feature (#632)

    • Add convenience bindings for dask dataframes and pyspark dataframes (#651)

  • Bugfixes

    • Fix documentation build and feature table in sphinx (#637, #631, #627)

    • Add scripts to API documentation

    • Skip dask test for older python versions (#649)

    • Add missing distributor keyword (#648)

    • Fix tuple input for cwt (#645)

Version 0.14.1

  • Fix travis deployment

Version 0.14.0

  • Breaking Change

    • Replace Benjamini-Hochberg implementation with statsmodels implementation (#570)

  • Refactoring and Documentation

    • travis.yml (#605)

    • gitignore (#608)

    • Fix docstring of c3 (#590)

    • Feature/pep8 (#607)

  • Added Features

    • Improve test coverage (#609)

    • Add “autolag” parameter to augmented_dickey_fuller() (#612)

  • Bugfixes

    • Feature/pep8 (#607)

    • Fix filtering on warnings with multiprocessing on Windows (#610)

    • Remove outdated logging config (#621)

    • Replace Benjamini-Hochberg implementation with statsmodels implementation (#570)

    • Fix the kernel and the naming of a notebook (#626)

Version 0.13.0

  • Drop python 2.7 support (#568)

  • Fixed bugs

    • Fix cache in friedrich_coefficients and agg_linear_trend (#593)

    • Added a check for wrong column names and a test for this check (#586)

    • Make sure to not install the tests folder (#599)

    • Make sure there is at least a single column which we can use for data (#589)

    • Avoid division by zero in energy_ratio_by_chunks (#588)

    • Ensure that get_moment() uses float computations (#584)

    • Preserve index when column_value and column_kind not provided (#576)

    • Add @set_property(“input”, “pd.Series”) when needed (#582)

    • Fix off-by-one error in longest strike features (fixes #577) (#578)

    • Add set_property import (#572)

    • Fix typo (#571)

    • Fix indexing of melted normalized input (#563)

    • Fix travis (#569)

  • Remove warnings (#583)

  • Update to newest python version (#594)

  • Optimizations

    • Early return from change_quantiles if ql >= qh (#591)

    • Optimize mean_second_derivative_central (#587)

    • Improve performance with Numpy’s sum function (#567)

    • Optimize mean_change (fixes issue #542) and correct documentation (#574)

Version 0.12.0

  • fixed bugs

    • wrong calculation of friedrich coefficients

    • feature selection selected too many features

    • an ignored max_timeshift parameter in roll_time_series

  • add deprecation warning for python 2

  • added support for index based features

  • new feature calculator

    • linear_trend_timewise

  • enable the RelevantFeatureAugmenter to be used in cross validated pipelines

  • increased scipy dependency to 1.2.0

Version 0.11.2

  • change chunking in energy_ratio_by_chunks to use all data points

  • fix warning for spkt_welch_density

  • adapt default settings for “value_count” and “range_count”

  • added

    • maxlag parameter to agg_autocorrelation function

  • now, the kind column of the input DataFrame is cast as str, old derived FC_Settings can become invalid

  • only set default_fc_parameters to ComprehensiveFCParameters() if also kind_to_fc_parameters is set None in extract_features

  • removed pyscaffold

  • use asymptotic algorithm to derive kendal tau

Version 0.11.1

  • general performance improvements

  • removed hard pinning of dependencies

  • fixed bugs

    • the stock price forecasting notebook

    • the multi classification notebook

Version 0.11.0

  • new feature calculators:

    • fft_aggregated

    • cid_ce

  • renamed mean_second_derivate_central to mean_second_derivative_central

  • add warning if no relevant features were found in feature selection

  • add columns_to_ignore parameter to from_columns method

  • add distribution module, contains support for distributed feature extraction on Dask

Version 0.10.1

  • split test suite into unit and integration tests

  • fixed the following bugs

    • use name of value column as time series kind

    • prevent the spawning of subprocesses which lead to high memory consumption

    • fix deployment from travis to pypi

Version 0.10.0

  • new feature calculators:

    • partial autocorrelation

  • added list of calculated features to documentation

  • added two ipython notebooks to

    • illustrate PCA on features

    • illustrate the Benjamini Yekutieli procedure

  • fixed the following bugs

    • improperly quotation of dickey fuller settings

Version 0.9.0

  • new feature calculators:

    • ratio_beyond_r_sigma

    • energy_ratio_by_chunks

    • number_crossing_m

    • c3

    • angle & abs for fft coefficients

    • agg_autocorrelation

    • p-Value and usedLag for augmented_dickey_fuller

    • change_quantiles

  • changed the calculation of the following features:

    • fft_coefficients

    • autocorrelation

    • time_reversal_asymmetry_statistic

  • removed the following feature calculators:

    • large_number_of_peak

    • mean_autocorrelation

    • mean_abs_change_quantiles

  • add support for multi classification in the feature selection

  • improved description of the rolling mechanism

  • added function make_forecasting_frame method for forecasting tasks

  • internally ditched the pandas representation of the time series, yielding drastic speed improvements

  • replaced feature calculator types from aggregate/aggregate with parameter/apply to simple/combiner

  • add test for the ipython notebooks

  • added notebook to inspect dft features

  • make sure that RelevantFeatureAugmentor always imputes

  • fixed the following bugs

    • impute was replacing whole columns by mean

    • fft coefficient were only calculated on truncated part

    • allow to suppress warnings from impute function

    • added missing lag in time_reversal_asymmetry_statistic

Version 0.8.1

  • new features:

    • linear trend

    • agg trend

  • new sklearn compatible transformers

    • PerColumnImputer

  • fixed bugs

    • make mannwhitneyu method compatible with scipy > v0.18.0

  • added caching to travis

  • internally, added serial calculation of features

Version 0.8.0

  • Breaking API changes:

    • removing of feature extraction settings object, replaced by keyword arguments and a plain dictionary (fc_parameters)

    • removing of feature selection settings object, replaced by keyword arguments

  • added notebook with examples of new API

  • added chapter in docs about the new API

  • adjusted old notebooks and documentation to new API

Version 0.7.1

  • added a maximum shift parameter to the rolling utility

  • added a FAQ entry about how to use tsfresh on windows

  • drastically decreased the runtime of the following features

    • cwt_coefficient

    • index_mass_quantile

    • number_peaks

    • large_standard_deviation

    • symmetry_looking

  • removed baseline unit tests

  • bugfixes:

    • per sample parallel imputing was done on chunks which gave non deterministic results

    • imputing on dtypes other that float32 did not work properly

  • several improvements to documentation

Version 0.7.0

  • new rolling utility to use tsfresh for time series forecasting tasks

  • bugfixes:

    • index_mass_quantile was using global index of time series container

    • an index with same name as id_column was breaking parallelization

    • friedrich_coefficients and max_langevin_fixed_point were occasionally stalling

Version 0.6.0

  • progress bar for feature selection

  • new feature: estimation of largest fixed point of deterministic dynamics

  • new notebook: demonstration how to use tsfresh in a pipeline with train and test datasets

  • remove no logging handler warning

  • fixed bug in the RelevantFeatureAugmenter regarding the evaluate_only_added_features parameters

Version 0.5.0

  • new example: driftbif simulation

  • further improvements of the parallelization

  • language improvements in the documentation

  • performance improvements for some features

  • performance improvements for the impute function

  • new feature and feature renaming: sum_of_recurring_values, sum_of_recurring_data_points

Version 0.4.0

  • fixed several bugs: checking of UCI dataset, out of index error for mean_abs_change_quantiles

  • added a progress bar denoting the progress of the extraction process

  • added parallelization per sample

  • added unit tests for comparing results of feature extraction to older snapshots

  • added “high_comp_cost” attribute

  • added ReasonableFeatureExtraction settings only calculating features without “high_comp_cost” attribute

Version 0.3.1

  • fixed several bugs: closing multiprocessing pools / index out of range cwt calculator / division by 0 in index_mass_quantile

  • now all warnings are disabled by default

  • for a singular type time series data, the name of value column is used as feature prefix

Version 0.3.0

  • fixed bug with parsing of “NUMBER_OF_CPUS” environment variable

  • now features are calculated in parallel for each type

Version 0.2.0

  • now p-values are calculated in parallel

  • fixed bugs for constant features

  • allow time series columns to be named 0

  • moved uci repository datasets to github mirror

  • added feature calculator sample_entropy

  • added MinimalFeatureExtraction settings

  • fixed bug in calculation of fourier coefficients

Version 0.1.2

  • added support for python 3.5.2

  • fixed bug with the naming of the features that made the naming of features non-deterministic

Version 0.1.1

  • mainly fixes for the read-the-docs documentation, the pypi readme and so on

Version 0.1.0

  • Initial version :)