v1.7.5

New Features

• Add MFLES Method @tblume1992 (#810)
• add SklearnModel @jmoralez (#812)

Bug Fixes

• Fix allowdrift and allowmean in non-stepwise AutoARIMA @manuel-calzolari (#828)
• return Naive model for constant series in AutoCES @jmoralez (#821)
• disable decomposition in theta when data has less than two seasonal periods @jmoralez (#820)
• return max float instead of Inf in arima_css_op @jmoralez (#819)

Enhancement

• fix syntax warnings in refit check @jmoralez (#824)
• set target_col=None in _parse_X_level @jmoralez (#822)
• fix refit check for models forward @jmoralez (#823)
• Create CODE_OF_CONDUCT.md @tracykteal (#818)
• use single threaded functions in multiprocessing @jmoralez (#815)

v1.7.4

Bug Fixes

• tbats fixes @jmoralez (#788)
• fix: parallel custom cols @AzulGarza (#790)

Documentation

• specify conformal intervals restrictions with respect to series lengths in tutorial @tonysinghmss (#795)

Enhancement

• persist distributed fitted values @jmoralez (#808)

v1.7.3

Bug Fixes

• fix fitted values for sparse models @jmoralez (#775)

v1.7.2

New Features

• AutoTBATS, experiment, and minor issues @MMenchero (#759)

Bug Fixes

• MSTL fixes @jmoralez (#766)
• Fix X computation for prediction intervals @jcatankard (#755)
• fix search_arima @jmoralez (#752)

Enhancement

• Address division by zero warnings in arima @jmoralez (#770)
• check exogenous features are present when needed @jmoralez (#768)
• add predict_in_sample to intermittent models @jmoralez (#764)
• remove njit from simple functions @jmoralez (#754)

v1.7.1

New Features

• TBATS model @MMenchero (#578)

Bug Fixes

• fix arima init params @jmoralez (#747)
• remove seasonal component in CES when data has less than two periods @jmoralez (#746)
• fix predict_in_sample for seasonal naive @jmoralez (#744)
• ensure float dtype in ets @jmoralez (#741)

Enhancement

• download dataset from s3 in peak forecasting tutorial @jmoralez (#745)
• add phi to AutoETS init signature @jmoralez (#742)
• use future instead of deprecation warnings @jmoralez (#739)

v1.7.0

New Features

• support integer refit in cross_validation @jmoralez (#731)
• support forecast_fitted_values in distributed @jmoralez (#732)
• use environment variable to get id as column in outputs @jmoralez (#721)
• support different column names for ids, times and targets @jmoralez (#718)
• add mstl_decomposition function @jmoralez (#701)
• [FEAT] Ability to save and load StatsForecast @akmalsoliev (#667)
• support caching all jitted functions @jmoralez (#651)

Bug Fixes

• fix incorrect computation of n in ARIMA @StatKumar (#708)
• fix predict_insample docstrings @jmoralez (#713)
• fix arima var_coef @jmoralez (#706)
• feat: _calculate_sigma() explicitly handles series with 1 data point (n=0) @cparmet (#699)
• [Feat] Robustified crossvalidation @kdgutier (#668)
• support exogenous features in distributed @jmoralez (#638)
• fix MSTL where trend forecaster supports level @jmoralez (#625)

Documentation

• run how-to docs @jmoralez (#730)
• run getting-started docs @jmoralez (#726)
• add exogenous features support to models table @jmoralez (#714)
• use numpy style in all docstrings @jmoralez (#695)
• Fix Minimal example and ARIMA family heading @yibenhuang (#694)
• fix equations in documentation @jmoralez (#697)
• Isolated import cells [Issue #653] @patrick-dd (#691)
• MLFlow Tutorial @kvnkho (#676)
• Add Mintlify automations @hahnbeelee (#671)
• Correct broken urls in docs @DaveParr (#660)
• Fixed error in GARCH tutorial that corresponds to issue #629 @MMenchero (#650)
• Restoring example using Conformal Prediction on StatsForecast @kvnkho (#643)
• arima model user guide @Naren8520 (#570)

Dependencies

• remove pyarrow dependency for polars @jmoralez (#705)
• [UPDATE] Updated Docker & requirements.txt @akmalsoliev (#672)
• [CHORE] Updating requirements.txt @akmalsoliev (#666)
• test support python 3.11 @msfidelis (#425)

Enhancement

• use frequency validation from utilsforecast @jmoralez (#717)
• improve deprecation error messages @jmoralez (#715)
• migrate plot and generate_series to utils @jmoralez (#636)

v1.6.0

Republish of the 1.6.0 release from August 23rd 2023, since it disappeared from github.

New Features

• Conformal Prediction @kvnkho (#592)
• Adding levels to distributed backends @kvnkho (#581)
• [FEAT] Add ConstantModel and ZeroModel @FedericoGarza (#568)
• [FEAT] Add NaNModel @FedericoGarza (#567)
• [FEAT] Add conformal intervals Theta family @FedericoGarza (#501)
• [FEAT] Add conformal intervals for CES @FedericoGarza (#500)
• [FEATURE] Polars support @akmalsoliev (#448)
• [FEAT] Add conformal intervals to arima family @FedericoGarza (#488)
• [FEAT] Add conformal intervals to StatsForecast class @FedericoGarza (#487)
• [FEAT] Add conformal prediction for AutoARIMA @FedericoGarza (#486)
• [FEAT] Return plot object @FedericoGarza (#465)
• [FEAT] Add stl_kwargs to MSTL @FedericoGarza (#462)
• [FEAT] Support pandas 2.0 changes @FedericoGarza (#456)

Breaking Change

• Reducing StatsForecast Size @kvnkho (#600)

Bug Fixes

• take shallow copy on dataframe processing and fix get_cmap deprecation @jmoralez (#617)
• fix arima max order params @jmoralez (#613)
• Fix iteration range in non-stepwise AutoARIMA @manuel-calzolari (#601)
• [Core] Fixed RuntimeWarning Generated by _get_cols (#538) @taniishkaaa (#563)
• [FIX] Unnecessary datetime column conversion @akmalsoliev (#558)
• [FIX] Accommodated switch to jupyter-lab @akmalsoliev (#511)
• [FIX] Polars hotfix @akmalsoliev (#503)
• [FIX] Added polars to settings.ini @akmalsoliev (#499)
• [FIX] HoltWinters forecasts (weekly seasonality) @FedericoGarza (#483)
• [FIX] Consider correct seasonality for exp smoothing @FedericoGarza (#474)
• Remove unused levels from categorical unique_id @nickto (#473)
• [FIX] Add protection ETS zero division error @FedericoGarza (#470)
• [FIX] allow period=1 using mstl @FedericoGarza (#463)
• [FIX] ets forbidden component combinations @FedericoGarza (#461)
• [FIX] Different results between forecast and fit/predict MSTL @FedericoGarza (#446)
• Stop using mutable defaults for ets_f lower and upper arguments @kschmaus (#437)
• [FIX] Distributed behaviour with exogenous variables @FedericoGarza (#427)

Documentation

• Adding details to Conformal Prediction docs @kvnkho (#607)
• Tutorial for Conformal Prediction @kvnkho (#597)
• [FIX] SeasonalNaive docs @nelsoncardenas (#588)
• Fix incorrect parameter name in How-To-Guides @yibenhuang (#584)
• Changing Load Forecasting Data Souce @kvnkho (#572)
• [DOCS] Adding GARCH and ARCH to index @kvnkho (#571)
• Fix broken doc links @andrewgross (#566)
• Fixing broken links @kvnkho (#559)
• Updated the document to reflect the deprecation of ETS in favor of AutoETS (#319) @taniishkaaa (#561)
• [DOC] renamed files for order @mergenthaler (#554)
• Update nbs/ @FedericoGarza (#548)
• [FIX] Restructure how-to guides @FedericoGarza (#547)
• [DOCS] AutomaticForecasting @mergenthaler (#545)
• Updating Distributed Documentation @kvnkho (#541)
• Update nbs/ @FedericoGarza (#546)
• [FEAT] New docs structure @FedericoGarza (#534)
• [DOCS] Polars documentation @akmalsoliev (#527)
• Update nbs/docs/contribute/ file @FedericoGarza (#544)
• Update nbs/docs/contribute/ file @FedericoGarza (#543)
• Update nbs/ file @FedericoGarza (#542)
• Update CONTRIBUTING.md file @FedericoGarza (#533)
• [FEAT] Add mlforecast to ensemble example @FedericoGarza (#502)
• [FIX] Link end to end pipeline @FedericoGarza (#477)
• Update README.md @mergenthaler (#468)
• [DOCS] Added more instructions on nbdev @akmalsoliev (#449)
• [DOCS] Hide utils fns from core @FedericoGarza (#429)
• Fix naive model description @shagn (#268)

Enhancement

• check for level when prediction_intervals are set @jmoralez (#615)
• raise informative error when series are too short for cross_validation @jmoralez (#610)
• Add release drafter @FedericoGarza (#514)
• Add issue template files @FedericoGarza (#513)
• Add issue template @FedericoGarza (#512)

v1.5.0

What's Changed

Features

New models

• [FEAT] ARIMA model (no auto version) in #383
• [FEAT] AutoRegressive model in #387
• [FEAT] GARCH and ARCH models in #403

New functionality

Forward methods

Now you can pre-train a model and use new data to make forecasts through the forward method. Supported models:

• [FEAT] Add forward method to Theta models in #362
• [FEAT] Add forward method to ETS models in #363
• [FEAT] Add forward method to AutoCES class in #364
• [FEAT] Add forward method to MSTL model in #369
• [FEAT] Add forward method to AutoARIMA (ARIMA and AutoRegressive) in #368

Misc

• [FEAT] Add alias argument to models (fit the same instance of models with different names) in #357
• [FEAT] Add cross-validation without refit (using the forward method) in #370
• [FEAT] Allow seasonality greater than 24 for ETS in #384
• [FEAT] Allow passing fixed coefficients for Arima in #386
• [FEAT] AutoCES prediction intervals in #394 (now StatsForecast is fully probabilistic)
• [FEAT] Add cla workflow in #351
• [FEAT] Add pypi downloads badge in #352
• [FEAT] Ignore jupyter notebooks as part of languages in #356
• [FEAT] Add nbdev merge to git attributes in #365
• [FEAT] Add citation in #366
• [FEAT] Update table of models in #396

Experiments

• [FEAT] Add M5 and M4-Daily experiments (Amazon Forecast) in #332
• [FEAT] Test recover M3 performance in #385
• [FEAT] BigQuery comparison in #421
• [FEAT] Experiments for ETS prediction intervals for multiple confidence levels in #377
• [FEAT] Add M3 experiment in #348
• [FEAT] Add a test ensuring the m3 performance is recovered in less than two minutes in #388

Tutorials

• [FEAT] Improved intermittent data nb in #359
• [FEAT] Add statistical and neural methods tutorial in #399
• [FEAT] Improve anomaly detection nb in #338
• [FEAT] GARCH and ARCH models tutorial in #418
• [FEAT] Improved notebook on prediction intervals in #358
• [FEAT] Improved notebook on exogenous regressors in #392
• [FEAT] Improve documentation in #376

Fixes

• [FIX] Exponential Smoothing description in #346
• [FIX] Changed dataset and model to make example easier to follow in #345
• [FIX] Readme M3 typo in #350
• [FIX] Delete CLA.yml in #355
• [FIX] Broken link in #360
• [FIX] Clean aws nbs in #361
• [FIX] Add correct link to hierarchicalforecast by #372
• [FIX] Recover table-based documentation (core nb, compatible with docstrings) in #374
• [FIX] update sklearn -> scikit-learn in #375
• [FIX] Ray CI in #381
• [FIX] Links and typos in documentation in #390
• [FIX] Correct evaluation using Winkler score by @MMenchero in #395
• [FIX] Recover plots prediction intervals tutorial in #398
• [FIX] Use https links instead of s3 uris (stat-neural tutorial) in #400
• [FIX] New nbdev clean behaviour in #412
• [FIX] Model imports in #408

New dependencies

• [FEAT] plotly-resampler as plotting engine in #354
• [FEAT] Move Fugue to core dependency in #419

New Contributors

• @jvdd made their first contribution in #354
• @Roymprog made their first contribution in #390
• @nelsoncardenas made their first contribution in #408

Full Changelog: v1.4.0...v1.5.0

v1.4.0

What's Changed

• feat: Added prediction intervals for insample and ETS models in #328
• [FEAT] Add plot anomalies option in #341
• [DOCS] Improve README and docs page index in #344

Full Changelog: v1.3.2...v1.4.0

v1.3.2

What's Changed

• [FIX] Improvements to StatsForecast's plot method in #312
• [FEAT] Add plotly as engine to StatsForecast's plot method in #313
• [FEAT] Add autowidth to plotly engine in #314
• feat: add new documentation in #317
• [FIX] ETS for inttermitent series in #315
• [FIX] Theta for intermittent series in #316
• [FEAT] Rename ETS to AutoETS in #318
• [FEAT] Change library to newest black formatting in #320
• [FIX] Add new plot method to mstl example in #324
• [FIX] Build docs for Theta model in #322
• [FIX] Isolate elements for all subplots plotly in #323
• Fix/multiple seas docs in #325
• [FEAT] Add mstl experiment in #326
• [FIX] Prevent futurewarning series indexing in #327
• Fix sidebar in #331
• feat: Improved tutorial on Cross-Validation in #333
• Feat/improve prediction intervals in #336
• fix: Improved AutoARIMA plot in #334
• docs: ERCOT electricity demand peak forecasting in #335
• docs: fix peak demand plot in #339

New Contributors

• @cchallu made their first contribution in #335

Full Changelog: v1.3.1...v1.3.2