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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