[Core] Categorical Features

[NudnikShpilkis]

What happened + What you expected to happen

According to the Exogenous Variables Tutorial:

When including exogenous variables always use a scaler by setting the scaler_type hyperparameter. The scaler will scale all the temporal features: the target variable y, historic and future variables.

That means that all non-static variables are scaled and treated as real numbers. Neural-Forecast should give the option of specifying categorical features that should be embedded and non-categorical features that should be scaled. Using the example from the tutorial gen_forecast should be scaled, while week_day should be treated as a categorical.

Versions / Dependencies

1.6.4

Reproduction script

https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html

Issue Severity

Medium: It is a significant difficulty but I can work around it.

[rd1886]

Do we have a workaround for this in the meantime?

Thanks!

[candalfigomoro]

I think the handling of categorical exogenous variables is one of the few areas where neuralforecast is lacking. Other libraries such as Darts https://github.com/unit8co/darts allow you to specify for different models (e.g. TFT) what the static categorical variables etc. are so that they can be handled appropriately, however neuralforecast does not provide any ad hoc handling for categorical variables.