Hello team! We just made a new release of neuralforecast with some pretty important changes: New features - New model: TimeXer, a Transformer-based model specifically designed to handle exogenous features - 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 - Improve docstrings of all models - Minor bug fix in TFT: we can omit specifying an RNN type and the static covariate encoder will still work. - Fitting with an invalid validation size now print a nice error message - Add bfloat16 support - Recurrent models can now produce forecasts recursively or directly. - IQLoss now gives monotonic quantiles - MASE loss now works Breaking Changes - Unify API - 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 - Multivariate models do not error when predicting when

n_series

>

batch_size

- Insample prediction works with series of varying lengths Documentation - Big overhaul of the documentation to remove old and deprecated code. - Add example of modifying the default

configure_optimizers()

behavior (use of

ReduceLROnPlateau

scheduler) This release solves many pain points from our users and it adds features that were aksed for a long time. Most of these features come from @Olivier Sprangers massive PR so many thanks for that and all your work on the documentation.