Changelog

tsfresh uses Semantic Versioning

Unreleased

  • Bugfixes:
    • Disable multiprocessing for n_jobs=1 (#852)

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 :)