CHANGELOG.rst

Changelog

All notable changes to this project will be documented in this file. We keep track of changes in this file since v0.4.0. The format is based on Keep a Changelog and we adhere to Semantic Versioning. The source code for all releases is available on GitHub.

[0.6.0] - 2021-04-15

Fixed

• Fix counting for Github's automatic language discovery (#812) @xuyxu
• Fix counting for Github's automatic language discovery (#811) @xuyxu
• Fix examples CI checks (#793) @mloning
• Fix TimeSeriesForestRegressor (#777) @mloning
• Fix Deseasonalizer docstring (#737) @mloning
• SettingWithCopyWarning in Prophet with exogenous data (#735) @jschemm
• Correct docstrings for check_X and related functions (#701) @Lovkush-A
• Fixed bugs mentioned in #694 (#697) @AidenRushbrooke
• fix typo in CONTRIBUTING.md (#688) @luiszugasti
• Fix duplicacy in the contribution's list (#685) @afzal442
• HIVE-COTE 1.0 fix (#678) @MatthewMiddlehurst

Changed

• Update sklearn version (#810) @mloning
• Remove soft dependency check for numba (#808) @mloning
• Modify tests for forecasting reductions (#756) @Lovkush-A
• Upgrade nbqa (#794) @MarcoGorelli
• Enhanced exception message of splitters (#771) @aiwalter
• Enhance forecasting model selection/evaluation (#739) @mloning
• Pin PyStan version (#751) @mloning
• master to main conversion in docs folder closes #644 (#667) @ayan-biswas0412
• Update governance (#686) @mloning
• remove MSM from unit tests for now (#698) @TonyBagnall
• Make update_params=true by default (#660) @pabworks
• update dataset names (#676) @TonyBagnall

Added

• Add support for exogenous variables to forecasting reduction (#757) @mloning
• Added forecasting docstring examples (#772) @aiwalter
• Added the agg argument to EnsembleForecaster (#774) @Ifeanyi30
• Added OptionalPassthrough transformer (#762) @aiwalter
• Add doctests (#766) @mloning
• Multiplexer forecaster (#715) @koralturkk
• Upload source tarball to PyPI during releases (#749) @dsherry
• Create developer guide (#734) @mloning
• Refactor TSF classifier into TSF regressor (#693) @luiszugasti
• Outlier detection with HampelFilter (#708) @aiwalter
• changes to contributing.md to include directions to installation (#695) @kanand77
• Evaluate (example and fix) (#690) @aiwalter
• Knn unit tests (#705) @TonyBagnall
• Knn transpose fix (#689) @TonyBagnall
• Evaluate forecaster function (#657) @aiwalter
• Multioutput reduction strategy for forecasting (#659) @Lovkush-A

All contributors: @AidenRushbrooke, @Ifeanyi30, @Lovkush-A, @MarcoGorelli, @MatthewMiddlehurst, @TonyBagnall, @afzal442, @aiwalter, @ayan-biswas0412, @dsherry, @jschemm, @kanand77, @koralturkk, @luiszugasti, @mloning, @pabworks and @xuyxu

[0.5.3] - 2021-02-06

Fixed

• Fix reduced regression forecaster reference (#658) @mloning
• Address Bug #640 (#642) @patrickzib
• Ed knn (#638) @TonyBagnall
• Euclidean distance for KNNs (#636) @goastler

Changed

• Pin NumPy 1.19 (#643) @mloning
• Update CoC committee (#614) @mloning
• Benchmarking issue141 (#492) @ViktorKaz
• Catch22 Refactor & Multithreading (#615) @MatthewMiddlehurst

Added

• Create new factory method for forecasting via reduction (#635) @Lovkush-A
• Feature ForecastingRandomizedSearchCV (#634) @pabworks
• Added Imputer for missing values (#637) @aiwalter
• Add expanding window splitter (#627) @koralturkk
• Forecasting User Guide (#595) @Lovkush-A
• Add data processing functionality to convert between data formats (#553) @RNKuhns
• Add basic parallel support for ElasticEnsemble (#546) @xuyxu

All contributors: @Lovkush-A, @MatthewMiddlehurst, @RNKuhns, @TonyBagnall, @ViktorKaz, @aiwalter, @goastler, @koralturkk, @mloning, @pabworks, @patrickzib and @xuyxu

[0.5.2] - 2021-01-13

Fixed

• Fix ModuleNotFoundError issue (#613) @Hephaest
• Fixes _fit(X) in KNN (#610) @TonyBagnall
• UEA TSC module improvements 2 (#599) @TonyBagnall
• Fix sktime.classification.frequency_based not found error (#606) @Hephaest
• UEA TSC module improvements 1 (#579) @TonyBagnall
• Relax numba pinning (#593) @dhirschfeld
• Fix fh.to_relative() bug for DatetimeIndex (#582) @aiwalter

All contributors: @Hephaest, @MatthewMiddlehurst, @TonyBagnall, @aiwalter and @dhirschfeld

[0.5.1] - 2020-12-29

Added

• Add ARIMA (#559) @HYang1996
• Add fbprophet wrapper (#515) @aiwalter
• Add MiniRocket and MiniRocketMultivariate (#542) @angus924
• Add Cosine, ACF and PACF transformers (#509) @afzal442
• Add example notebook Window Splitters (#555) @juanitorduz
• Add SlidingWindowSplitter visualization on doctrings (#554) @juanitorduz

Fixed

• Pin pandas version to fix pandas-related AutoETS error on Linux (#581) @mloning
• Fixed default argument in docstring in SlidingWindowSplitter (#556) @ngupta23

All contributors: @HYang1996, @TonyBagnall, @afzal442, @aiwalter, @angus924, @juanitorduz, @mloning and @ngupta23

[0.5.0] - 2020-12-19

Added

• Add tests for forecasting with exogenous variables (#547) @mloning
• Add HCrystalBall wrapper (#485) @MichalChromcak
• Tbats (#527) @aiwalter
• Added matrix profile using stumpy (#471) @utsavcoding
• User guide (#377) @mloning
• Add GitHub workflow for building and testing on macOS (#505) @mloning
• [DOC] Add dtaidistance (#502) @mloning
• Implement the feature_importances_ property for RISE (#497) @AaronX121
• Add scikit-fda to the list of related software (#495) @vnmabus
• [DOC] Add roadmap to docs (#467) @mloning
• Add parallelization for RandomIntervalSpectralForest (#482) @AaronX121
• New Ensemble Forecasting Methods (#333) @magittan
• CI run black formatter on notebooks as well as Python scripts (#437) @MarcoGorelli
• Implementation of catch22 transformer, CIF classifier and dictionary based clean-up (#453) @MatthewMiddlehurst
• Added write dataset to ts file functionality (#438) @whackteachers
• Added ability to load from csv containing long-formatted data (#442) @AidenRushbrooke
• Transform typing (#420) @mloning

Changed

• Refactoring utils and transformer module (#538) @mloning
• Update README (#454) @mloning
• Clean up example notebooks (#548) @mloning
• Update README.rst (#536) @aiwalter
• [Doc]Updated load_data.py (#496) @Afzal-Ind
• Update forecasting.py (#487) @raishubham1
• update basic motion description (#475) @vollmersj
• [DOC] Update docs in benchmarking/data.py (#489) @Afzal-Ind
• Edit Jupyter Notebook 01_forecasting (#486) @bmurdata
• Feature & Performance improvements of SFA/WEASEL (#457) @patrickzib
• Moved related software from wiki to docs (#439) @mloning

Fixed

• Fixed issue outlined in issue 522 (#537) @ngupta23
• Fix plot-series (#533) @gracewgao
• added mape_loss and cosmetic fixes to notebooks (removed kernel) (#500) @tch
• Fix azure pipelines (#506) @mloning
• [DOC] Fix broken docstrings of RandomIntervalSpectralForest (#473) @AaronX121
• Add back missing bibtex reference to classifiers (#468) @whackteachers
• Avoid seaborn warning (#472) @davidbp
• Bump pre-commit versions, run again on notebooks (#469) @MarcoGorelli
• Fix series validation (#463) @mloning
• Fix soft dependency imports (#446) @mloning
• Fix bug in AutoETS (#445) @HYang1996
• Add ForecastingHorizon class to docs (#444) @mloning

Removed

• Remove manylinux1 (#458) @mloning

All contributors: @AaronX121, @Afzal-Ind, @AidenRushbrooke, @HYang1996, @MarcoGorelli, @MatthewMiddlehurst, @MichalChromcak, @TonyBagnall, @aiwalter, @bmurdata, @davidbp, @gracewgao, @magittan, @mloning, @ngupta23, @patrickzib, @raishubham1, @tch, @utsavcoding, @vnmabus, @vollmersj and @whackteachers

[0.4.3] - 2020-10-20

Added

• Support for 3d numpy array (#405) @mloning
• Support for downloading dataset from UCR UEA time series classification data set repository (#430) @Emiliathewolf
• Univariate time series regression example to TSFresh notebook (#428) @evanmiller29
• Parallelized TimeSeriesForest using joblib. (#408) @kkoziara
• Unit test for multi-processing (#414) @kkoziara
• Add date-time support for forecasting framework (#392) @mloning

Changed

• Performance improvements of dictionary classifiers (#398) @patrickzib

Fixed

• Fix links in Readthedocs and Binder launch button (#416)

@mloning * Fixed small bug in performance metrics (#422) @krumeto * Resolved warnings in notebook examples (#418) @alwinw * Resolves #325 ModuleNotFoundError for soft dependencies (#410) @alwinw

All contributors: @Emiliathewolf, @alwinw, @evanmiller29, @kkoziara, @krumeto, @mloning and @patrickzib

[0.4.2] - 2020-10-01

Added

• ETSModel with auto-fitting capability (#393) @HYang1996
• WEASEL classifier (#391) @patrickzib
• Full support for exogenous data in forecasting framework (#382) @mloning, (#380) @mloning
• Multivariate dataset for US consumption over time (#385) @SebasKoel
• Governance document (#324) @mloning, @fkiraly

Fixed

• Documentation fixes (#400) @brettkoonce, (#399) @akanz1, (#404) @alwinw

Changed

• Move documentation to ReadTheDocs with support for versioned documentation (#395) @mloning
• Refactored SFA implementation (additional features and speed improvements) (#389) @patrickzib
• Move prediction interval API to base classes in forecasting framework (#387) @big-o
• Documentation improvements (#364) @mloning
• Update CI and maintenance tools (#394) @mloning

All contributors: @HYang1996, @SebasKoel, @fkiraly, @akanz1, @alwinw, @big-o, @brettkoonce, @mloning, @patrickzib

[0.4.1] - 2020-07-09

Added

• New sktime logo @mloning
• TemporalDictionaryEnsemble (#292) @MatthewMiddlehurst
• ShapeDTW (#287) @Multivin12
• Updated sktime artwork (logo) @mloning
• Truncation transformer (#315) @ABostrom
• Padding transformer (#316) @ABostrom
• Example notebook with feature importance graph for time series forest (#319) @HYang1996
• ACSF1 data set (#314) @BandaSaiTejaReddy
• Data conversion function from 3d numpy array to nested pandas dataframe (#304) @vedazeren

Changed

• Replaced gunpoint dataset in tutorials, added OSULeaf dataset (#295) @marielledado
• Updated macOS advanced install instructions (#306) (#308) @sophijka
• Updated contributing guidelines (#301) @Ayushmaanseth

Fixed

• Typos (#293) @Mo-Saif, (#285) @Pangoraw, (#305) @hiqbal2
• Manylinux wheel building (#286) @mloning
• KNN compatibility with sklearn (#310) @Cheukting
• Docstrings for AutoARIMA (#307) @btrtts

All contributors: @Ayushmaanseth, @Mo-Saif, @Pangoraw, @marielledado, @mloning, @sophijka, @Cheukting, @MatthewMiddlehurst, @Multivin12, @ABostrom, @HYang1996, @BandaSaiTejaReddy, @vedazeren, @hiqbal2, @btrtts

[0.4.0] - 2020-06-05

Added

• Forecasting framework, including: forecasting algorithms (forecasters), tools for composite model building (meta-forecasters), tuning and model evaluation
• Consistent unit testing of all estimators
• Consistent input checks
• Enforced PEP8 linting via flake8
• Changelog
• Support for Python 3.8
• Support for manylinux wheels

Changed

• Revised all estimators to comply with common interface and to ensure scikit-learn compatibility

Removed

• A few redundant classes for the series-as-features setting in favour of scikit-learn's implementations: Pipeline and GridSearchCV
• HomogeneousColumnEnsembleClassifier in favour of more flexible ColumnEnsembleClassifier

Fixed

• Deprecation and future warnings from scikit-learn
• User warnings from statsmodels