Nixtla/neuralforecast v3.0.0 on GitHub

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.

[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

[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

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

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