tsfresh uses Semantic Versioning

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 :)