v3.1.5

New features

• [FEAT] Explainability for multivariate models @marcopeix (#1464)
• [FEAT] Add XLinear @marcopeix (#1445)

Fixes

• [FIX] Add protection against duplicated level and quantiles in distribution losses @marcopeix (#1448)
• [FIX] Inverse scaling with MASE loss @marcopeix (#1447)
• [FIX] Exogenous support in TimeXer @marcopeix (#1444)
• [FIX] Correct min_samples formula in conformal prediction for step_size @W057 (#1461)
• [FIX] Fix save() to prevent FileExistsError with DDP @W057 (#1460)

Documentation

• [DOC] update title @deven367 (#1459)

v3.1.4

Changes

• [CHORE] Version bump @marcopeix (#1437)
• [CHORE]: fix requirements to build whl @deven367 (#1436)

v3.1.3

Features

• [FEAT] Support normalization for static exogenous features @JuditHalperin (#1406)
• [FEAT] Add support for historical and static exogenous in TimeXer @marcopeix (#1404)

Bug fixes

• [FIX] Prevent inverse normalization of quantile validation loss @marcopeix (#1432)
• [FIX] Fix broken links @deven367 (#1433)
• [FIX] Fix type of n_windows in cross_validation @Riklia (#1378)
• [FIX] Apply windows batch size @marcopeix @elephaint (#1429)

Documentation

• Updates contributing for building documentation @nasaul (#1430)
• Improve grammar and readability in documentation @rogeliomj (#1423)
• Add explanability to the sidebar @deven367 (#1428)
• Fixed missing import @ngupta23 (#1425)
• Migrate to docs.json @deven367 (#1422)
• Lazydocs to mkdocstrings @deven367 (#1399)
• Fix table render @deven367 (#1409)

Changes

• [CHORE] Drop Python 3.9, add Python 3.13 @marcopeix (#1434)

Many thanks to @nasaul for his work on the CI! We usually don't list these PRs in the release notes, but thanks to his work, tests run better for all contributors.

v3.1.2

Features

• [FEAT] Any horizon explanation @marcopeix (#1393)

Bug fixes

• [FIX] Remove torch fix @elephaint (#1392)

v3.1.1

Hotfix

• [FIX] Backwards compatibility with saved models breaks if "explain" attribute isn't present @elephaint (#1389)

v3.1.0

New features

• [FEAT] (Shapley) explanations for univariate forecast models @elephaint @marcopeix (#1377)
• [FEAT] Any horizon prediction @elephaint @JQGoh (#1368)
• [FEAT]: Training window filtering @marcopeix (#1344)
• [FEAT] xLSTM @elephaint (#1363)

Bug fixes

• [FIX]: torch version @marcopeix (#1380)
• [FIX] Fixes for using uv @elephaint (#1364)
• [FIX] Bugs ISQF @elephaint (#1358)
• Update BiTCN doc dropout to match code @LemuelKL (#1376)
• Adds support for static variables in predict_insample method @nasaul (#1349)
• rm unused y_insample arg from pytorch losses @deven367 (#1360)

General

• Dev environment installation using uv @JQGoh (#1362)
• Enhancements @deven367 (#1371)
• Codespace setup for Neuralforecast @JQGoh (#1382)
• migrate tests from nbs to pytest @deven367 (#1353)

Documentation

• New documentation pipeline @deven367 (#1369)

v3.0.2

Enhancements

• Distributional predictions in predict_insample() @janovergoor (#1309)
• Optimizations in tsdataset: reduce allocations for large datasets @tylernisonoff (#1335)

Fixes

• [FIX]: Add logic to load custom models when using ReduceLROnPlateau @marcopeix (#1340)
• [FIX]: Fixes incorrect cuts in conformal prediction with conformal_error @elephaint (#1331)

v3.0.1

Features

• FEAT: Select basis functions in NBEATS @tblume1992 @marcopeix (#1191)
• FEAT: Add flash-attention @LeonEthan (#1295)
• FEAT: HuberIQLoss @elephaint (#1307)

Bug Fixes

• FIX: Fix iPython version @elephaint (#1282)
• FIX: Recurrent predictions @elephaint (#1285)
• FIX: Fix poor performance with the NegativeBinomial DistributionLoss @JQGoh (#1289)
• FIX: Add exclude_insample_y param to TimeXer for model loading @marcopeix (#1306)
• FIX: Set 2.0.0<=pytorch<=2.6.0 to avoid conflicts with networkx with Python 3.9 @marcopeix (#1318)
• FIX: Create windows once @elephaint (#1325)
• FIX: Add h_train to RNNs & fix issue with input_size @elephaint (#1326)
• FIX: Allow static vars only with NBEATSx and exogenous block @marcopeix (#1319)

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)