tsfresh uses Semantic Versioning
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- fixed bug with parsing of "NUMBER_OF_CPUS" environment variable
- now features are calculated in parallel for each type
- 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
- added support for python 3.5.2
- fixed bug with the naming of the features that made the naming of features non-deterministic
- mainly fixes for the read-the-docs documentation, the pypi readme and so on
- Initial version :)