PyPI · #3357 most downloaded on PyPI

statsforecast

2.1.11.5M downloads/moNixtla/statsforecast ↗New releases in your daily digest, changelog included.

Time series forecasting suite using statistical models

Last release 2 months ago
16 Jul 2026
Release timing varies
gaps range from 2 weeks to 9 months
Nearly every release is documented
notes for 40 of 43 stable releases
Nothing withdrawn
no release was ever pulled
5 years old
43 releases · first in 2021

Release timeline

43 releases since 2021

202120222023202420252026

20212026

One column per quarter.

Releases

2.1.116 Jul 2026
arima: fixes José Morales (@jmoralez)
Changes

arima: fixes José Morales (@jmoralez) (#1173)

[CHORE] Publish Python 3.14 wheels Saul (@nasaul) (#1188)
Open source →
2.1.014 Jul 2026Deprecated
[FEAT] Deprecate Numba @elephaint
Features

[FEAT] Add conformal_error prediction intervals method @DresdenGman (#1175)

[FEAT] Unified error distributions across the StatsForecast suite (ETS, CES, Theta + shared C++/Python infra) @nasaul (#1116)

[FEAT] ConformalSeasonalPool model (CSP) @nasaul (#1159)

[FEAT] Adds t, ged, normal-skewed and laplace distributions for fitting ARIMA @nasaul (#1113)

[FEAT] Add UCM (Unobserved Components Model) support @formeo (#1081)

[FEAT] Deprecate Numba @elephaint (#1099)

[FEAT] Simulate method for sample trajectories @ankitlade12 (#1072)

Bug Fixes

[FIX] Add py.typed marker @DresdenGman (#1176)

[FIX] IndexError in MSTL with more than 6 seasonal periods @Vedant-Agarwal (#1163)

[FIX] Validate unique model names for models without alias @HummerQAQ (#1079)

[FIX] Prevent NaN prediction intervals in theta for short series (len(y) == 4) @robrui (#1142)

[FIX] Add new level validation method @MMenchero (#1157)

[FIX] Align SeasonalNaive forecasts when len(y) < season_length @MMenchero (#1153)

[FIX] Improve adida & imapa @elephaint (#1097)

[FIX] Prevent out-of-bounds write when initializing residuals in arima… @vkverma9534 (#1073)

Docs

[DOCS] Translate Spanish comment to English in SimpleExponentialSmoothing notebook @kalichiruvelli-art (#1129)

[DOCS] Fix remaining links @deven367 (#1127)

[DOCS] broken imgs @deven367 (#1120)

[DOCS] Add missing tutorial for AutoARIMAProphet adapter @MMenchero (#1117)

[DOCS] Update title @deven367 (#1093)

[DOCS] Fix broken figure links and fix table rendering @deven367 (#1086)

[DOCS] Use utilsforecast.evaluation.evaluate in notebooks @IndarKarhana (#1084)

[DOCS] Remove extra double quote in link @loama (#1067)

[DOCS] Migrate docs json @deven367 (#1069)

[DOCS] lazydocs to mkdocstrings @deven367 (#1060)

[DOCS] Adds VN1 notebook @nasaul (#1052)

[DOCS] Fix table render @deven367 (#1066)

[DOCS]Third party licenses @deven367 (#1065)

[DOCS] Adds VN1 notebook @nasaul (#1052)

Chores

[CHORE] Updates pyarrow @nasaul (#1161)

[CHORE] Bumps idna @nasaul (#1156)

[CHORE] SHA-pin remaining third-party Actions in pytest/lint/build-docs/ci @shaun0927 (#1138)

[CHORE] update link to datasetsforecast @deven367 (#1132)

[CHORE] Adds explicit permissions to workflows @nasaul (#1112)

[CHORE] Update dependencies for alerts @nasaul (#1111)

[CHORE] update pyproject.toml and other workflows to use uv @deven367 (#1080)

[CHORE] Updates Contributing.md @nasaul (#1075)

[CHORE] remove blog @deven367 (#1076)

[CHORE] Unpin scipy @nasaul (#1074)

[CHORE] Minor fixes @deven367 (#1071)

[CHORE] Fixes ets name @nasaul (#1070)

[CHORE] Third party licenses @deven367 (#1065)

2.0.329 Oct 2025

Documentation improvement Saul (@nasaul)

Changes

Documentation improvement Saul (@nasaul) (#1046)
docs: enhance conformal prediction tutorial for SEO Khuyen Tran (@khuyentran1401) (#1053)
Solves testing issues Saul (@nasaul) (#1051)
Fix more broken links Deven Mistry (@deven367) (#1040)
typo: README.md Eduardo López (@loama) (#1045)
doc enhancements Deven Mistry (@deven367) (#1039)
New documentation pipeline Deven Mistry (@deven367) (#1037)
migrate tests from nbs to pytest Deven Mistry (@deven367) (#1029)
rm df arg from constructor Deven Mistry (@deven367) (#1030)
[FEAT] Add python 3.13 Olivier Sprangers (@elephaint) (#1028)
SeasonalNaive.forward Kin (@kdgutier) (#1025)

Open source →

2.0.25 Jul 2025Deprecated

FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead. Vaibhav Gupta (@vaidatascientist)

Bug fixes

Pin statsmodels / scipy Olivier Sprangers (@elephaint) (#1019)

Documentation

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

Open source →

2.0.118 Feb 2025

fix(nelder-mead): argsort before early exit José Morales (@jmoralez)

Bug Fixes

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

Documentation

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

Enhancement

enh(SES): improve implementation Christopher Titchen (@christophertitchen) (#973)
enh: migrate theta to c++ José Morales (@jmoralez) (#955)

Open source →

2.0.026 Nov 2024Deprecated

breaking: remove deprecated behavior José Morales (@jmoralez)

Breaking Change

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

Bug Fixes

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

Documentation

Update broken yahoo query Paul Labonne (@paullabonne) (#925)

Open source →

1.7.819 Sept 2024

fix: chunk series in parallel forecast José Morales (@jmoralez)

Bug Fixes

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

Open source →

1.7.7.113 Sept 2024

fix: bump build to include submodules in sdist José Morales (@jmoralez)

Bug Fixes

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

Open source →

1.7.713 Sept 2024

fix: reduce overhead in parallel forecast José Morales (@jmoralez)

Bug Fixes

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

Enhancement

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

Open source →

1.7.617 Jul 2024

fix matrix product for arima var_coef José Morales (@jmoralez)

Bug Fixes

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

Documentation

mfles model reference and experiment José Morales (@jmoralez) (#853)
Fix SyntaxWarning: invalid escape sequence Ravi Brock (@ravibrock) (#842)

Dependencies

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

Enhancement

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

Open source →

1.7.524 May 2024

Fix allowdrift and allowmean in non-stepwise AutoARIMA @manuel-calzolari

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)

Open source →

1.7.48 Apr 2024

fix: parallel custom cols @AzulGarza

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)

Open source →

1.7.35 Feb 2024

fix fitted values for sparse models @jmoralez

Bug Fixes

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

Open source →

1.7.231 Jan 2024

AutoTBATS, experiment, and minor issues @MMenchero

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)

Open source →

1.7.15 Jan 2024Deprecated

use future instead of deprecation warnings @jmoralez

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)

Open source →

1.7.019 Dec 2023Deprecated

improve deprecation error messages @jmoralez

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)

Open source →

1.6.023 Aug 2023Deprecated

take shallow copy on dataframe processing and fix get_cmap deprecation @jmoralez

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)

Open source →

1.5.028 Feb 2023

[FEAT] ARIMA model (no auto version) in https://github.com/Nixtla/statsforecast/pull/383

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

Open source →

1.4.01 Dec 2022

feat: Added prediction intervals for insample and ETS models in https://github.com/Nixtla/statsforecast/pull/328

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

Open source →

1.3.228 Nov 2022

[FIX] Improvements to StatsForecast's plot method in https://github.com/Nixtla/statsforecast/pull/312

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

Open source →

1.3.117 Nov 2022

[FEAT] Add plot method to StatsForecast class in https://github.com/Nixtla/statsforecast/pull/305

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

Open source →

1.3.015 Nov 2022

[FIX] Use conda env for ray tests in https://github.com/Nixtla/statsforecast/pull/297

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

Open source →

1.2.12 Nov 2022

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

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

Open source →

1.2.031 Oct 2022

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

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

Open source →

1.1.325 Oct 2022

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

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

Open source →

1.1.224 Oct 2022

[FEAT] Improve navbar docs in https://github.com/Nixtla/statsforecast/pull/262

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

Open source →

1.1.15 Oct 2022

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

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

Open source →

1.1.028 Sept 2022

[FIX] License in https://github.com/Nixtla/statsforecast/pull/191

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

Open source →

1.0.015 Aug 2022Breaking

[BREAKING CHANGE] SKLearn syntax in https://github.com/Nixtla/statsforecast/pull/184 * Full Changelog: https://github.com/Nixtla/statsforecast/compare…

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

Open source →

0.7.123 Jul 2022

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

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

Open source →

0.7.021 Jul 2022Breaking

[BREAKING CHANGE] Fitted Values Computation in https://github.com/Nixtla/statsforecast/pull/137

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

Open source →

0.6.019 Jul 2022Breaking

[BREAKING CHANGE] Deprecate python3.6 in https://github.com/Nixtla/statsforecast/pull/146

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

Open source →

0.5.627 Jun 2022

[DOCS] Typo fixes by @ryanrussell in https://github.com/Nixtla/statsforecast/pull/117

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

Open source →

0.5.59 May 2022

ARIMA level/quantile compatibility, missing nbdev_flow, protected gif by @kdgutier in https://github.com/Nixtla/statsforecast/pull/102

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

Open source →

0.5.42 May 2022

feat: add issues template by @FedericoGarza in https://github.com/Nixtla/statsforecast/pull/93

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

Open source →

0.5.312 Apr 2022

summary method for the AutoARIMA class requested in #31.

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.

Open source →

0.5.219 Mar 2022

Added predict_in_sample method for AutoARIMA.

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

Open source →

0.5.111 Mar 2022

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

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

Open source →

0.5.07 Mar 2022

Inclusion of prediction intervals for auto_arima.

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).

Open source →

0.4.01 Mar 2022

Inclusion of exogenous variables for auto_arima.

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.

Open source →

0.3.020 Feb 2022

Nothing published for this version

0.2.09 Dec 2021

Nothing published for this version

0.1.024 Nov 2021

Nothing published for this version

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statsforecast

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