v1.5.0

What's Changed

Release focusing on:

• python 3.9 end-of-life
• changes to testing framework.
• New estimators in pytorch-forecasting v1 and beta v2.

See full changelog

New Contributors

• @Vishnu-Rangiah made their first contribution in #1905
• @Pinaka07 made their first contribution in #1914
• @hubkrieb made their first contribution in #1910
• @Himanshu-Verma-ds made their first contribution in #1488
• @lohraspco made their first contribution in #1926
• @zju-ys made their first contribution in #1924
• @caph1993 made their first contribution in #1944
• @szepeviktor made their first contribution in #1948
• @sanskarmodi8 made their first contribution in #1978

All Contributors

@agobbifbk,
@caph1993,
@cngmid,
@fkiraly,
@fnhirwa,
@Himanshu-Verma-ds,
@hubkrieb,
@jdb78,
@lohraspco,
@phoeenniixx,
@Pinaka07,
@PranavBhatP,
@sanskarmodi8,
@Sohaib-Ahmed21,
@szepeviktor
@Vishnu-Rangiah,
@zju-ys

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

v1.4.0

What's Changed

Feature and maintenance update.

See full changelog

New Contributors

• @gbilleyPeco made their first contribution in #1750
• @pietsjoh made their first contribution in #1399
• @MartinoMensio made their first contribution in #1579
• @phoeenniixx made their first contribution in #1811
• @cngmid made their first contribution in #1827
• @Marcrb2 made their first contribution in #1518
• @jobs-git made their first contribution in #1864

All Contributors

@agobbifbk,
@Borda,
@cngmid,
@fkiraly,
@fnhirwa,
@gbilleyPeco,
@jobs-git,
@Marcrb2,
@MartinoMensio,
@phoeenniixx,
@pietsjoh,
@PranavBhatP

Full Changelog: v1.3.0...v1.4.0

v1.3.0

What's Changed

Feature and maintenance update.

• python 3.13 support
• tide model
• bugfixes for TFT

See full changelog

New Contributors

• @xiaokongkong made their first contribution in #1719
• @madprogramer made their first contribution in #1720
• @julian-fong made their first contribution in #1705
• @Sohaib-Ahmed21 made their first contribution in #1734
• @d-schmitt made their first contribution in #1580
• @Luke-Chesley made their first contribution in #1516
• @PranavBhatP made their first contribution in #1762

All Contributors

@d-schmitt,
@fkiraly,
@fnhirwa,
@julian-fong,
@Luke-Chesley,
@madprogramer,
@PranavBhatP,
@Sohaib-Ahmed21,
@xiaokongkong,
@XinyuWuu

Full Changelog: v1.2.0...v1.3.0

v1.2.0

What's Changed

Maintenance update, minor feature additions and bugfixes.

• support for numpy 2.X
• end of life for python 3.8
• fixed documentation build
• bugfixes

See full changelog

New Contributors

• @ewth made their first contribution in #1696
• @airookie17 made their first contribution in #1692
• @benHeid made their first contribution in #1704
• @eugenio-mercuriali made their first contribution in #1699

All Contributors

@airookie17,
@benHeid,
@eugenio-mercuriali,
@ewth,
@fkiraly,
@fnhirwa,
@XinyuWuu,
@yarnabrina

Full Changelog: v1.1.1...v1.2.0

v1.1.1

What's Changed

Hotfix release to correct typo in package name in pyproject.toml, to correct pytorch-forecasting PEP 440 identifier.

Otherwise identical with 1.1.0

Full Changelog: v1.1.0...v1.1.0

v1.1.0

What's Changed

Maintenance update widening compatibility ranges and consolidating dependencies:

• support for python 3.11 and 3.12, added CI testing
• support for MacOS, added CI testing
• core dependencies have been minimized to numpy, torch, lightning, scipy, pandas, and scikit-learn.
• soft dependencies are available in soft dependency sets: all_extras for all soft dependencies, and tuning for optuna based optimization.

Dependency changes

• the following are no longer core dependencies and have been changed to optional dependencies : optuna, statsmodels, pytorch-optimize, matplotlib. Environments relying on functionality requiring these dependencies need to be updated to install these explicitly.
• optuna bounds have been updated to optuna >=3.1.0,<4.0.0
• optuna-integrate is now an additional soft dependency, in case of optuna >=3.3.0

Deprecations and removals

• from 1.2.0, the default optimizer will be changed from "ranger" to "adam" to avoid non-torch dependencies in defaults. pytorch-optimize optimizers can still be used. Users should set the optimizer explicitly to continue using "ranger".
• from 1.1.0, the loggers do not log figures if soft dependency matplotlib is not present, but will raise no exceptions in this case. To log figures, ensure that matplotlib is installed.

All Contributors

@andre-marcos-perez,
@avirsaha,
@bendavidsteel,
@benHeid,
@bohdan-safoniuk,
@Borda,
@CahidArda,
@fkiraly,
@fnhirwa,
@germanKoch,
@jacktang,
@jdb78,
@jurgispods,
@maartensukel,
@MBelniak,
@orangehe,
@pavelzw,
@sfalkena,
@tmct,
@XinyuWuu,
@yarnabrina,

New Contributors

• @jurgispods made their first contribution in #1366
• @jacktang made their first contribution in #1353
• @andre-marcos-perez made their first contribution in #1346
• @tmct made their first contribution in #1340
• @bohdan-safoniuk made their first contribution in #1318
• @MBelniak made their first contribution in #1230
• @CahidArda made their first contribution in #1175
• @bendavidsteel made their first contribution in #1359
• @Borda made their first contribution in #1498
• @fkiraly made their first contribution in #1598
• @XinyuWuu made their first contribution in #1599
• @pavelzw made their first contribution in #1407
• @yarnabrina made their first contribution in #1630
• @fnhirwa made their first contribution in #1646
• @avirsaha made their first contribution in #1649

Full Changelog: v1.0.0...v1.1.0

Update to pytorch 2.0

Breaking Changes

• Upgraded to pytorch 2.0 and lightning 2.0. This brings a couple of changes, such as configuration of trainers. See the lightning upgrade guide. For PyTorch Forecasting, this particularly means if you are developing own models, the class method epoch_end has been renamed to on_epoch_end and replacing model.summarize() with ModelSummary(model, max_depth=-1) and Tuner(trainer) is its own class, so trainer.tuner needs replacing. (#1280)
• Changed the predict() interface returning named tuple - see tutorials.

Changes

• The predict method is now using the lightning predict functionality and allows writing results to disk (#1280).

Fixed

• Fixed robust scaler when quantiles are 0.0, and 1.0, i.e. minimum and maximum (#1142)

Poetry update

Fixed

• Removed pandoc from dependencies as issue with poetry install (#1126)
• Added metric attributes for torchmetric resulting in better multi-GPU performance (#1126)

Added

• "robust" encoder method can be customized by setting "center", "lower" and "upper" quantiles (#1126)

Multivariate networks

Added

• DeepVar network (#923)
• Enable quantile loss for N-HiTS (#926)
• MQF2 loss (multivariate quantile loss) (#949)
• Non-causal attention for TFT (#949)
• Tweedie loss (#949)
• ImplicitQuantileNetworkDistributionLoss (#995)

Fixed

• Fix learning scale schedule (#912)
• Fix TFT list/tuple issue at interpretation (#924)
• Allowed encoder length down to zero for EncoderNormalizer if transformation is not needed (#949)
• Fix Aggregation and CompositeMetric resets (#949)

Changed

• Dropping Python 3.6 suppport, adding 3.10 support (#479)
• Refactored dataloader sampling - moved samplers to pytorch_forecasting.data.samplers module (#479)
• Changed transformation format for Encoders to dict from tuple (#949)

Contributors

• jdb78

Bugfixes

Fixed

• Fix with creating tensors on correct devices (#908)
• Fix with MultiLoss when calculating gradient (#908)

Contributors

• jdb78