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Recent Releases of https://github.com/nixtla/statsforecast

https://github.com/nixtla/statsforecast - v2.0.2

Bug fixes

  • Pin statsmodels / scipy @elephaint (#1019)

Documentation

  • docs: Updated Theta tutorials @MMenchero (#1005)
  • FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead. @vaidatascientist (#991)
  • Added '~' in the code cell to accurately get the anomalies in AnomalyDetection.ipynb @vaidatascientist (#990)

- Python
Published by github-actions[bot] 11 months ago

https://github.com/nixtla/statsforecast - v2.0.1

Bug Fixes

  • fix(nelder-mead): argsort before early exit @jmoralez (#984)
  • fix(arima): handle zero sigma2 in CSS method @andrewscottm (#960)
  • fix(arima): initialize residuals to zero for CSS @jmoralez (#959)

Documentation

  • Typo: Removed mean with fitted in doc string of `predictinsampl… @Gunnvant (#978)
  • docs(readme): add TBATS and MFLES @jmoralez (#954)
  • docs(README): update pandas freq @jmoralez (#951)

Enhancement

  • enh(SES): improve implementation @christophertitchen (#973)
  • enh: migrate theta to c++ @jmoralez (#955)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/statsforecast - v2.0.0

Breaking Change

  • breaking: remove deprecated behavior @jmoralez (#946)
  • breaking: allowmean and allowdrift True by default in AutoARIMA @V-nguard (#918)

Bug Fixes

  • C++ ARIMA fixes and refactoring @filipcacky (#939)
  • fix: seasonal naive confidence interval bug @andrewscottm (#932)
  • fix: auto arima xreg rank deficient test @andrewscottm (#931)

Documentation

  • Update broken yahoo query @paullabonne (#925)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/statsforecast - v1.7.8

Bug Fixes

  • fix: chunk series in parallel forecast @jmoralez (#915)
  • fix: set lower bound on threadpoolctl @jmoralez (#914)
  • fix: static requirements @jmoralez (#910)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/statsforecast - v1.7.7.1

Bug Fixes

  • fix: bump build to include submodules in sdist @jmoralez (#906)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/statsforecast - v1.7.7

Bug Fixes

  • fix: reduce overhead in parallel forecast @jmoralez (#901)
  • fix: pass constant in autoarima stepwise @robert-robison (#899)

Enhancement

  • enh: keep target dtype in output @jmoralez (#865)
  • enh: migrate ARIMA to C++ @jmoralez (#895)
  • enh: migrate ETS to C++ @jmoralez (#757)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/statsforecast - v1.7.6

Bug Fixes

  • fix matrix product for arima var_coef @jmoralez (#848)
  • fix arima trained models results index @jmoralez (#858)
  • support multiple seasonalities in mstl_decomposition @jmoralez (#861)
  • propagate model alias to fallback model in fit @jmoralez (#846)

Documentation

  • mfles model reference and experiment @jmoralez (#853)
  • Fix SyntaxWarning: invalid escape sequence @ravibrock (#842)

Dependencies

  • config: CI, add python 3.12 @westonplatter (#793)

Enhancement

  • remove TqdmExperimentalWarning @jmoralez (#854)
  • store time taken by model in forecast method @jmoralez (#850)
  • support progress bar in parallel forecast @jmoralez (#849)

- Python
Published by github-actions[bot] almost 2 years ago

https://github.com/nixtla/statsforecast - 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 arimacssop @jmoralez (#819)

Enhancement

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

- Python
Published by github-actions[bot] about 2 years ago

https://github.com/nixtla/statsforecast - 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)

- Python
Published by github-actions[bot] about 2 years ago

https://github.com/nixtla/statsforecast - v1.7.3

Bug Fixes

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

- Python
Published by github-actions[bot] over 2 years ago

https://github.com/nixtla/statsforecast - 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 predictinsample to intermittent models @jmoralez (#764)
  • remove njit from simple functions @jmoralez (#754)

- Python
Published by github-actions[bot] over 2 years ago

https://github.com/nixtla/statsforecast - 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 predictinsample 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)

- Python
Published by github-actions[bot] over 2 years ago

https://github.com/nixtla/statsforecast - v1.7.0

New Features

  • support integer refit in cross_validation @jmoralez (#731)
  • support forecastfittedvalues 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: calculatesigma() 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)

- Python
Published by github-actions[bot] over 2 years ago

https://github.com/nixtla/statsforecast - 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 getcols (#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)

- Python
Published by jmoralez over 2 years ago

https://github.com/nixtla/statsforecast - v1.5.0

What's Changed

Features

New models

  • [FEAT] ARIMA model (no auto version) in https://github.com/Nixtla/statsforecast/pull/383
  • [FEAT] AutoRegressive model in https://github.com/Nixtla/statsforecast/pull/387
  • [FEAT] GARCH and ARCH models in https://github.com/Nixtla/statsforecast/pull/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 https://github.com/Nixtla/statsforecast/pull/362 * [FEAT] Add forward method to ETS models in https://github.com/Nixtla/statsforecast/pull/363 * [FEAT] Add forward method to AutoCES class in https://github.com/Nixtla/statsforecast/pull/364 * [FEAT] Add forward method to MSTL model in https://github.com/Nixtla/statsforecast/pull/369 * [FEAT] Add forward method to AutoARIMA (ARIMA and AutoRegressive) in https://github.com/Nixtla/statsforecast/pull/368

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

Experiments

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

Tutorials

  • [FEAT] Improved intermittent data nb in https://github.com/Nixtla/statsforecast/pull/359
  • [FEAT] Add statistical and neural methods tutorial in https://github.com/Nixtla/statsforecast/pull/399
  • [FEAT] Improve anomaly detection nb in https://github.com/Nixtla/statsforecast/pull/338
  • [FEAT] GARCH and ARCH models tutorial in https://github.com/Nixtla/statsforecast/pull/418
  • [FEAT] Improved notebook on prediction intervals in https://github.com/Nixtla/statsforecast/pull/358
  • [FEAT] Improved notebook on exogenous regressors in https://github.com/Nixtla/statsforecast/pull/392
  • [FEAT] Improve documentation in https://github.com/Nixtla/statsforecast/pull/376

Fixes

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

New dependencies

  • [FEAT] plotly-resampler as plotting engine in https://github.com/Nixtla/statsforecast/pull/354
  • [FEAT] Move Fugue to core dependency in https://github.com/Nixtla/statsforecast/pull/419

New Contributors

  • @jvdd made their first contribution in https://github.com/Nixtla/statsforecast/pull/354
  • @Roymprog made their first contribution in https://github.com/Nixtla/statsforecast/pull/390
  • @nelsoncardenas made their first contribution in https://github.com/Nixtla/statsforecast/pull/408

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.4.0...v1.5.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.4.0

What's Changed

  • feat: Added prediction intervals for insample and ETS models in https://github.com/Nixtla/statsforecast/pull/328
  • [FEAT] Add plot anomalies option in https://github.com/Nixtla/statsforecast/pull/341
  • [DOCS] Improve README and docs page index in https://github.com/Nixtla/statsforecast/pull/344

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.3.2...v1.4.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.3.2

What's Changed

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

New Contributors

  • @cchallu made their first contribution in https://github.com/Nixtla/statsforecast/pull/335

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.3.1...v1.3.2

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.3.1

What's Changed

  • [FEAT] Add plot method to StatsForecast class in https://github.com/Nixtla/statsforecast/pull/305
  • [FEAT] New Issues Templates in https://github.com/Nixtla/statsforecast/pull/307
  • [FIX] make logging config local to package in https://github.com/Nixtla/statsforecast/pull/275
  • [FIX] Error when ds column is object in https://github.com/Nixtla/statsforecast/pull/309

New Contributors

  • @JeroenPeterBos made their first contribution in https://github.com/Nixtla/statsforecast/pull/275

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.3.0...v1.3.1

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.3.0

What's Changed

  • [FIX] Use conda env for ray tests in https://github.com/Nixtla/statsforecast/pull/297
  • [FIX] Source code broken links in https://github.com/Nixtla/statsforecast/pull/293
  • [FIX] Sparse models with zero-valued time series in https://github.com/Nixtla/statsforecast/pull/294
  • [FIX] Add explicit optional argument (PEP-484) in https://github.com/Nixtla/statsforecast/pull/301
  • [FIX] SeasonalNaive in https://github.com/Nixtla/statsforecast/pull/302
  • [FEAT] Add exogenous variables to fugue's backend in https://github.com/Nixtla/statsforecast/pull/300
  • [FEAT] Add Theta methods in https://github.com/Nixtla/statsforecast/pull/299
  • [FEAT] Add MSTL example and comparison in https://github.com/Nixtla/statsforecast/pull/295
  • [FEAT] Add backend argument to StatsForecast class in https://github.com/Nixtla/statsforecast/pull/303

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.2.1...v1.3.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.2.1

What's Changed

  • [FEAT]: Add fallback model to cross validation in https://github.com/Nixtla/statsforecast/pull/289

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.2.0...v1.2.1

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.2.0

What's Changed

  • [FEAT] MSTL model n https://github.com/Nixtla/statsforecast/pull/284

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.1.3...v1.2.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.1.3

What's Changed

  • [FEAT] Add progress bar for sequential tasks in https://github.com/Nixtla/statsforecast/pull/280

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.1.2...v1.1.3

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.1.2

What's Changed

  • [FEAT] Improve navbar docs in https://github.com/Nixtla/statsforecast/pull/262
  • [FEAT] Add ETS to spark results in https://github.com/Nixtla/statsforecast/pull/264
  • [FEAT] Improve CES results in https://github.com/Nixtla/statsforecast/pull/265
  • [FEAT] Add fallback model to distributed backends in https://github.com/Nixtla/statsforecast/pull/277
  • [FIX] Backend docs in https://github.com/Nixtla/statsforecast/pull/278

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.1.1...v1.1.2

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.1.1

What's Changed

  • [FEAT] Add Distributed post in https://github.com/Nixtla/statsforecast/pull/257
  • [FEAT] Fallback Model in https://github.com/Nixtla/statsforecast/pull/259

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.1.0...v1.1.1

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.1.0

What's Changed

  • [FIX] License in https://github.com/Nixtla/statsforecast/pull/191
  • [FIX] Add hide statement for ets cells in https://github.com/Nixtla/statsforecast/pull/192
  • [FEAT] New experiments neuralprophet in https://github.com/Nixtla/statsforecast/pull/195
  • [FIX] use ubuntu to deploy docs in https://github.com/Nixtla/statsforecast/pull/197
  • [FIX] Broken links in https://github.com/Nixtla/statsforecast/pull/203
  • [FEAT] Add linters and update contributing instructions in https://github.com/Nixtla/statsforecast/pull/205
  • [FIX] nbdev latest changes in https://github.com/Nixtla/statsforecast/pull/208
  • [FIX] python3.7 ci error in https://github.com/Nixtla/statsforecast/pull/214
  • fixing the argument name for external regressors in the example notebook in https://github.com/Nixtla/statsforecast/pull/200
  • [FIX] #210 in https://github.com/Nixtla/statsforecast/pull/213
  • Docstring based documentation in https://github.com/Nixtla/statsforecast/pull/209
  • [FIX] nbdev version until next release in https://github.com/Nixtla/statsforecast/pull/225
  • [FEAT] Prediction intervals for fitted values in https://github.com/Nixtla/statsforecast/pull/228
  • [FEAT] Add anomaly detection example in https://github.com/Nixtla/statsforecast/pull/229
  • [FEAT] Add single anomaly plot in https://github.com/Nixtla/statsforecast/pull/230
  • [FEAT] Add exogenous var use case and install instructions in https://github.com/Nixtla/statsforecast/pull/231
  • [FEAT] M5 scalability comparison in https://github.com/Nixtla/statsforecast/pull/232
  • Intervals for some simple methods in https://github.com/Nixtla/statsforecast/pull/201
  • [FEAT] Add prediction intervals example in https://github.com/Nixtla/statsforecast/pull/239
  • [FEAT] Auto CES model by in https://github.com/Nixtla/statsforecast/pull/238
  • [FIX] nbdev releases in https://github.com/Nixtla/statsforecast/pull/251
  • [FEAT] Add CES + ETS ensemble results in https://github.com/Nixtla/statsforecast/pull/252
  • [FIX] nbdev deploy to gihub pages in https://github.com/Nixtla/statsforecast/pull/253

New Contributors

  • @jattenberg made their first contribution in https://github.com/Nixtla/statsforecast/pull/200

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v1.0.0...v1.1.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/statsforecast - v1.0.0

What's Changed

  • Add FugueBackend in https://github.com/Nixtla/statsforecast/pull/157
  • [FEAT] Add neuralprophet experiment in https://github.com/Nixtla/statsforecast/pull/181
  • [FEAT] nbdev2 integration in https://github.com/Nixtla/statsforecast/pull/186
  • [BREAKING CHANGE] SKLearn syntax in https://github.com/Nixtla/statsforecast/pull/184
  • Full Changelog: https://github.com/Nixtla/statsforecast/compare/v0.7.1...v1.0.0

- Python
Published by FedericoGarza almost 4 years ago

https://github.com/nixtla/statsforecast - v0.7.1

What's Changed

  • [FEAT] Fitted df returns in-sample values in https://github.com/Nixtla/statsforecast/pull/158

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v0.7.0...v0.7.1

- Python
Published by FedericoGarza almost 4 years ago

https://github.com/nixtla/statsforecast - v0.7.0

What's Changed

  • [Fix]: prevent arima RuntimeWarnings in https://github.com/Nixtla/statsforecast/pull/136
  • [BREAKING CHANGE] Fitted Values Computation in https://github.com/Nixtla/statsforecast/pull/137
  • Now models return a dict instead a numpy array with mean and fitted values.

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v0.6.0...v0.7.0

- Python
Published by FedericoGarza almost 4 years ago

https://github.com/nixtla/statsforecast - v0.6.0

What's Changed

  • [FEAT] Add ETS model and experiments in https://github.com/Nixtla/statsforecast/pull/142
  • [BREAKING CHANGE] Deprecate python3.6 in https://github.com/Nixtla/statsforecast/pull/146
  • [FEAT] Ray experiment ets in https://github.com/Nixtla/statsforecast/pull/145
  • [FEAT] Readme updates to include ets in https://github.com/Nixtla/statsforecast/pull/148

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v0.5.6...v0.6.0

- Python
Published by FedericoGarza almost 4 years ago

https://github.com/nixtla/statsforecast - v0.5.6

What's Changed

  • [DOCS] Typo fixes by @ryanrussell in https://github.com/Nixtla/statsforecast/pull/117
  • [FEAT] Add fugue example by @goodwanghan in https://github.com/Nixtla/statsforecast/pull/111
  • [FEAT] add cross validation functionality by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/120
  • [FIX] #121 fitting autoarima on constant time series causes typeerror by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/122
  • [DOCS] add shagn as a contributor for bug by @allcontributors in https://github.com/Nixtla/statsforecast/pull/124
  • [FEAT] add integer ds compatibility for cross validation by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/123
  • [FEAT] Add n_windows argument for cross_validation method by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/131
  • [EXP] Add benchmarks at scale experiment by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/134

New Contributors

  • @ryanrussell made their first contribution in https://github.com/Nixtla/statsforecast/pull/117
  • @goodwanghan made their first contribution in https://github.com/Nixtla/statsforecast/pull/111

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v0.5.5...v0.5.6

- Python
Published by FedericoGarza almost 4 years ago

https://github.com/nixtla/statsforecast - v0.5.5

What's Changed

  • ARIMA level/quantile compatibility, missing nbdev_flow, protected gif by @kdgutier in https://github.com/Nixtla/statsforecast/pull/102
  • Add dependency hint for quick intro by @guerda in https://github.com/Nixtla/statsforecast/pull/106
  • [FEAT] Add AutoARIMA adapter for Prophet by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/114

New Contributors

  • @kdgutier made their first contribution in https://github.com/Nixtla/statsforecast/pull/102
  • @guerda made their first contribution in https://github.com/Nixtla/statsforecast/pull/106

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v0.5.4...v0.5.5

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/statsforecast - v0.5.4

What's Changed

  • feat: add issues template by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/93
  • refactor: use Pool instead of ProcessPoolExecutor by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/96
  • Feat: add ray integration by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/98
  • fix: add automatic n_jobs behavior by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/99
  • Creation of forecast dates improvement by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/101
  • Ray experiment by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/103
  • Update README.md by @mergenthaler in https://github.com/Nixtla/statsforecast/pull/104

Full Changelog: https://github.com/Nixtla/statsforecast/compare/v0.5.3...v0.5.4

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/statsforecast - v0.5.3

What's Changed

New features

  • summary method for the AutoARIMA class requested in #31.
  • representational string for the AutoARIMA fitted model, requested in #83.

Bug Fixes

  • [BUG] croston_sba #88 fixed in #89.

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/statsforecast - AutoARIMA predict_in_sample

  • Added predict_in_sample method for AutoARIMA.
  • Users can now compute in sample forecasts including prediction intervals.

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/statsforecast - AutoARIMA class

  • Now: Good Ol' sklearn syntax with model = AutoARIMA(); model.fit(y); model.predict(10).
  • Bug fixes.

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/statsforecast - Prediction intervals for AutoARIMA

Notable changes

  • Inclusion of prediction intervals for auto_arima.
  • statsforecast is now installable from conda-forge (conda install -c conda-forecast statsforecast, thanks to @sugatoray).

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/statsforecast - Exogenous variables for AutoARIMA

Notable changes

  • Inclusion of exogenous variables for auto_arima.
  • The StatsForecast class now handles exogenous variables.
  • This release allows developers to include more models that use exogenous variables.
  • Bug fixes.

- Python
Published by FedericoGarza over 4 years ago