v3.0.0

New features

• FEAT: TimeXer @marcopeix (#1267)
• All losses compatible with all types of models (e.g. univariate/multivariate, direct/recurrent) OR appropriate protection added.
• DistributionLoss now supports the use of quantiles in predict, allowing for easy quantile retrieval for all DistributionLosses.
• Mixture losses (GMM, PMM and NBMM) now support learned weights for weighted mixture distribution outputs.
• Mixture losses now support the use of quantiles in predict, allowing for easy quantile retrieval.
• Improved stability of ISQF by adding softplus protection around some parameters instead of using .abs.
• Unified API for any quantile or any confidence level during predict for both point and distribution losses.

Enhancements

• [DOCS] Docstrings @elephaint (#1279)
• FIX: Minor bug fix in TFT and a nicer error message for fitting with the wrong val_size @marcopeix (#1275)
• [FIX] Adds bfloat16 support @elephaint (#1265)
• Recurrent models can now produce forecasts recursively or directly.
• IQLoss now gives monotonic quantiles
• MASE loss now works

Breaking Changes

• [FIX] Unify API @elephaint (#1023)
• RMoK uses the revin_affine parameter instead of revine_affine. This was a typo in the previous version.
• All models now inherit the BaseModel class. This changes how we implement new models in neuralforecast.
• Recurrent models now require an input_size parameter.
• TCN and DRNN are now window models, not recurrent models
• We cannot load a recurrent model from a previous version to v3.0.0

Bug Fixes

• [FIX] Multivariate models give error when predicting when n_series > batch_size @elephaint (#1276)
• [FIX]: Insample predictions with series of varying lengths @marcopeix (#1246)

Documentation

• [DOCS] Update documentation @elephaint (#1274)
• [DOCS] Add example of modifying the default configure_optimizers() behavior (use of ReduceLROnPlateau scheduler) @JQGoh (#1015)

v2.0.1

Enhancements

• FEAT: Custom RNN layers for TFT @Yanam24 (#1230)
• FEAT: Add the horizon weighing to the distribution losses @mwamsojo (#1233)

Documentation

• DOCS: Add citation note @elephaint (#1244)
• fix: azul @AzulGarza (#1245)

v2.0.0

Breaking Change

• breaking: remove deprecated behavior @jmoralez (#1220)

v1.7.7

Bug Fixes

• [FIX] Backward compatibility: missing prediction_intervals @JQGoh (#1224)

v1.7.6

New Features

• [FEAT]: Support providing DataLoader arguments to optimize GPU usage @jasminerienecker (#1186)
• [FEAT]: Set activation function in GRN of TFT @marcopeix (#1175)
• [FEAT]: Conformal Predictions in NeuralForecast @JQGoh (#1171)

Bug Fixes

• [FIX]: Ability load models saved using versions before 1.7 @tylernisonoff (#1207)
• [FIX]: Conformal prediction issues @elephaint (#1179)
• [FIX]: Feature importance when using only hist_exog in TFT fails @elephaint (#1174)
• [FIX]: Remove unused output layer NBEATSx @elephaint (#1168)
• [FIX]: Fix Tweedie loss @elephaint (#1164)
• [FIX]: MLPMultivariate incorrect static_exog parsing @elephaint (#1170)
• [FIX]: Deprecate activation functions for GRU @marcopeix (#1198)

Documentation

• [DOC]: Tutorial on cross-validation @marcopeix (#1176)
• [DOC]: Build docs on release only @elephaint (#1183)

v1.7.5

New Features

• [FEAT]: Move RevIN class to common module @JQGoh (#1083)
• [FEAT]: Add RMoK @marcopeix (#1148)
• FEAT: TimeLLM is faster and supports more LLMs @ive2go (#1139)
• [FEAT]: TFT-Interpretability @amirouyanis (#1104)
• [FEAT]: ]Add support for the local file dataloader with the Automodels @jasminerienecker (#1095)

Bug Fixes

• [FIX] CV Refit works with non-standard column names @elephaint (#1149)
• [FIX]: replace self.pred_len with self.h @carusyte (#1129)
• [FIX]: only define static encoder when applicable in TFT @jmoralez (#1114)
• [FIX]: remove cast to float in scalers @jmoralez (#1115)
• [FIX]: timemixer shapes mismatch and doc update @carusyte @marcopeix (#1138)

Dependencies

• Bump pypa/gh-action-pypi-publish from 1.10.0 to 1.10.1 in the ci-dependencies group @dependabot (#1146)
• Bump the ci-dependencies group with 2 updates @dependabot (#1135)

v1.7.4

New Features

• [FEAT] - Add KAN @marcopeix (#999)
• [FEAT] - Add TimeMixer @marcopeix (#1071)
• [FEAT] - Add support for datasets that can't fit in memory @jasminerienecker (#1049)

Bug Fixes

• [FIX] ignore pytorch lightning's PossibleUserWarning @fabianbergermann (#1081)
• [FIX] bug in the NBEATSx exogenous basis stack @jasminerienecker (#1072)
• [FIX] Fix nbdev_version in test & environment @elephaint (#1089)

Documentation

• [DOCS] add tutorial for large dataset DataLoader @jasminerienecker (#1074)
• [DOCS] Restructure documentation @elephaint (#1063)
• [DOCS] Fix examples @elephaint (#1092)
• [DOCS] Fix tables @elephaint (#1090)
• [DOCS] Fix docs layout issues @elephaint (#1085)
• [DOCS] Fix issues @elephaint (#1082)

Dependencies

• Bump actions/setup-python from 5.1.0 to 5.1.1 in the ci-dependencies group @dependabot (#1067)
• use commit hash in actions and add dependabot updates @jmoralez (#1066)

v1.7.3

New Features

• [FEAT] ISQF @elephaint (#1019)
• [FEAT] - Add SOFTS model @marcopeix (#1024)
• [FEAT] Add option to support user defined learning rate scheduler for NeuralForecast Models @JQGoh (#998)
• [FEAT] Implicit Quantile Networks @elephaint (#1007)

Bug Fixes

• use assign argument if available in nn.Module.load_state_dict @jmoralez (#1032)
• update min_size in TimeSeriesDataset.append @jmoralez (#1033)
• fix num_tasks in spark integration @jmoralez (#1028)

Documentation

• fix: add tsmixer tutorial to sidebar @AzulGarza (#978)
• Update models in the README @candalfigomoro (#946)

Enhancement

• suppress warning when saving hyperparameters in base auto @jmoralez (#1034)
• automatically set refit_with_val when early stopping is enabled @jmoralez (#1031)

v1.7.2

New Features

• [FEAT] DeepNPTS model @elephaint (#990)
• [FEAT] TiDE model @elephaint (#971)

Bug Fixes

• [FIX] Refit after validation boolean @elephaint (#991)
• fix cross_validation results with uneven windows @jmoralez (#989)
• [FIX] fix wrong import doc PatchTST @elephaint (#967)
• [FIX] raise exception nbeats h=1 with stacks @elephaint (#966)

Enhancement

• reduce default warnings @jmoralez (#974)
• Create CODE_OF_CONDUCT.md @tracykteal (#972)

v1.7.1

New Features

• multi-node distributed training with spark @jmoralez (#935)
• [FEAT] Add BiTCN model @elephaint (#958)
• [FEAT] - Add iTransformer to neuralforecast @marcopeix (#944)
• [FEAT] Add MLPMultivariate model @elephaint (#938)

Bug Fixes

• [FIX] Fixes default settings of BiTCN @elephaint (#961)
• [FIX] HINT not producing coherent forecasts @elephaint (#964)
• [FIX] Fixes 948 multivariate predict/val issues when n_series > 1024 @elephaint (#962)
• handle exogenous variables of TFT in parent class @jmoralez (#959)
• fix early stopping in ray auto models @jmoralez (#953)
• fix cross_validation when the id is the index @jmoralez (#951)

Documentation

• add MLflow logging example @cargecla1 (#892)