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)

v1.7.0

New Features

• [FEAT] Added TSMixerx model @elephaint (#921)
• Add Time-LLM @marcopeix (#908)
• [FEAT] Added TSMixer model @elephaint (#914)
• Add option to support user defined optimizer for NeuralForecast Models @JQGoh (#901)
• [FEAT] Added NLinear model @ggattoni (#900)
• [FEAT] Added DLinear model @cchallu (#875)
• support refit in cross_validation @jmoralez (#842)
• use environment variable to get id as column in outputs @jmoralez (#841)
• support different column names for ids, times and targets @jmoralez (#838)
• polars support @jmoralez (#829)
• add callbacks to auto models @jmoralez (#795)

Bug Fixes

• [FIX] Avoid raised error for varied step_size parameter during predict_insample() @JQGoh (#933)
• [FIX] 926 auto ensure all models support alias and 924 Configuring hyperparameter space for Auto* Models @elephaint (#927)
• fix base_multivariate window generation @jmoralez (#907)
• Fix optuna multigpu @jmoralez (#889)
• support saving and loading models with alias @jmoralez (#867)
• [FIX] Polars .columns produces list rather than Pandas Index @akmalsoliev (#862)
• add missing models to filename dict @jmoralez (#856)
• ensure exogenous features are lists @jmoralez (#851)
• fix save with save_dataset=False @jmoralez (#850)
• copy config in optuna @jmoralez (#844)
• Fixed: Exception: max_epochs is deprecated, use max_steps instead. @twobitunicorn (#835)
• fix single column 2d array polars df @jmoralez (#830)
• move scalers to core @jmoralez (#813)
• [FIX] Default AutoPatchTST config @cchallu (#811)
• [FIX] ReVin Numerical Stability @dluuo (#781)
• On Windows, prevent long trial directory names @tg2k (#735)

Documentation

• removed documentation for missing argument @yarnabrina (#913)
• feat: Added cross-validation tutorial @MMenchero (#897)
• chore: update license to apache-2 @AzulGarza (#882)
• [FEAT] Model table in README @cchallu (#880)
• redirect to mintlify docs @jmoralez (#816)
• add missing models to documentation @jmoralez (#775)

Dependencies

• add windows to CI @jmoralez (#814)
• address future warnings @jmoralez (#898)
• use scalers from coreforecast @jmoralez (#873)
• add python 3.11 to CI @jmoralez (#839)

Enhancement

• Reduce device transfers @elephaint (#923)
• extract common methods to BaseModel @jmoralez (#915)
• remove TQDMProgressBar callback @jmoralez (#899)
• use fsspec in save and load methods @jmoralez (#895)
• Feature/Check input for NaNs when available_mask = 1 @JQGoh (#894)
• switch flake8 to ruff @Borda (#871)
• use future instead of deprecation warnings @jmoralez (#849)
• add frequency validation and futr_df debugging methods @jmoralez (#833)

v1.6.4

New Features

• TemporalNorm with ReVIN learnable parameters @kdgutier (#768)
• support optuna in auto models @jmoralez (#763)
• [FEAT] TimesNet model @cchallu (#757)
• add local_scaler_type @jmoralez (#754)
• [FEAT] Implementation of Exogenous - NBEATSx @akmalsoliev (#738)

Bug Fixes

• [FIX] futr_exog_list in Auto and HINT classes @cchallu (#773)
• fix off by one error in BaseRecurrent available_ts @KeAWang (#759)

Documentation

• [DOCS] Scaling tutorial @cchallu (#770)
• [DOCS] Auto hyperparameter selection with optuna @cchallu (#767)
• [DOCS] Update tutorials to v.1.6.3 @cchallu (#741)

Enhancement

• check futr_exog_list are in futr_df @jmoralez (#769)

v1.6.2

What's Changed

• [FEAT] Add horizon_weight parameter to losses and BasePointLoss in #704
• [FIX] Fix device error in horizon_weight in #706
• [FIX] Base Windows padding in #715
• [FIX] Fixed bug in validation loss scale in #720
• [FIX] Base recurrent valid loss on original scale in #721

Full Changelog: v1.6.1...v1.6.2

v1.6.1

New Models

• DeepAR
• FEDformer

New features

• Available Mask to specify missing data in input data frame.
• Improve fit and cross_validation methods with use_init_models parameter to restore models to initial parameters.
• Added robust losses: HuberLoss, TukeyLoss, HuberQLoss, and HuberMQLoss.
• Added Bernoulli DistributionLoss to build temporal classifiers.
• New exclude_insample_y parameter to all models to build models only based on exogenous regressors.
• Added dropout to NBEATSx and NHITS models.
• Improved predict method of windows-based models to create batches to control memory usage. Can be controlled with the new inference_windows_batch_size parameter.
• Improvements to the HINT family of hierarchical models: identity reconciliation, AutoHINT, and reconciliation methods in hyperparameter selection.
• Added inference_input_sizehyperparameter to recurrent-based methods to control historic length during inference to better control memory usage and inference times.

New tutorials and documentation

• Neuralforecast map and How-to add new models
• Transformers for time-series
• Predict insample tutorial
• Interpretable Decomposition
• Outlier Robust Forecasting
• Temporal Classification
• Predictive Maintenance
• Statistical, Machine Learning, and Neural Forecasting methods

Fixed bugs and new protections

• Fixed bug on MinMax scalers that returned NaN values when the mask had 0 values.
• Fixed bug on y_loc and y_scale being in different devices.
• Added early_stopping_steps to the HINT method.
• Added protection in the fit method of all models to stop training when training or validation loss becomes NaN. Print input and output tensors for debugging.
• Added protection to prevent the case val_check_step > max_steps from causing an error when early stopping is enabled.
• Added PatchTST to save and load methods dictionaries.
• Added AutoNBEATSx to core's MODEL_DICT.
• Added protection to the NBEATSx-i model where horizon=1 causes an error due to collapsing trend and seasonality basis.

v1.5.0

What's Changed

Features

New models

• [FEAT] VanillaTransformer, Autoformer in #469
• [FEAT] StemGNN in #456
• [FEAT] PatchTST in #485
• [FEAT] Informer, augment_calendar_df, set seeds in fit and predict in #463
• [FEAT] Hierarchical Forecasting Networks (HINT) in #489

Misc

• [FEAT] Added MSSE class to losses.pytorch notebook in #468
• [FEAT] Robustified Distribution Outputs in #492
• [FEAT] Added MS availability to augment_calendar_df function in #506
• [FEAT] Add alias argument in #502
• [FEAT] mean default distribution output in addition to quantiles in #529
• [FEAT] Predict insample in #528

Fixes

• [FIX] Remove fixed lib versions in #446
• [FIX] Fixed sCRPS in losses.pytorch notebook in #462
• [FIX] Compute validation loss per epoch in #507
• [FIX] MLP/Recurrent-based memory inference complications in #512
• [FIX] Fix error with inference_input_size in #536
• [FIX] Add instructions python version in #539
• [FIX] Predict dates bug in #540
• [FIX] Autoformer in #523
• [FIX] Removed duplicate from model collection list in #517

Tutorials and Docs

• [FEAT] Electricity Peak Detection in #450
• [FEAT] Add End to End Walkthrough tutorial in #472
• [DOCS] Improved HINT documentation, and broken links in #490
• [DOCS] HINT documentation in #491
• [DOCS] HINT: Updated Unit Test and Example Notebooks in #516
• [FEAT] HINT Unit Test in #499

New dependencies

• [FEAT] Add support for lightning>=2.0.0, and torch>=2.0.0 in #498
• [FEAT] Allow pandas 2 in #508

New Contributors

• @VinishUchiha made their first contribution in #517

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