Support for custom cross-validation strategies, e.g., forward chaining for temporally ordered tabular data

Hi AutoGluon team,

I'm using TabularPredictor for temporally ordered tabular data and need more flexible cross-validation options. The current CV methods and "groups" parameter don't suffice for many time-based scenarios.

Standard k-fold CV can cause data leakage with ordered data. As far as I can tell, I'm unable to do the following using the "groups" parameter:

fold 1: training [month 1], test [month 2] fold 2: training [months 1-2], test [month 3] fold 3: training [months 1-3], test [month 4] ...and so on

Proposed solution: Add a custom_cv_strategy parameter to TabularPredictor, allowing custom splitting functions or pre-implemented strategies like forward chaining.

Let me know if I've overlooked something, and this is actually currently possible, or if there is a workaround.

Otherwise, thank you for considering!