[Pseudocrat]

Depends on use case. Hybrid approaches have been dominating the M-Competitions, but there are generally small percentage differences in variance of statistical models vs machine learning models.

And exponentially higher cost for ML models.

[VHRanger]

At the end of the day, if training or doing inference on the ML model is massively more costly in time or compute, you'll iterate much less with it.

I also think it's a dead end to try to have foundation models for "time series" - it's a class of data! Like when people tried to have foundation models for any general graph type.

You could make foundation models for data within that type - eg. meteorological time series, or social network graphs. But for the abstract class type it seems like a dead end.

[rockinghigh]

These models may be helpful if they speed up convergence when fine tuned on business-specific time series.

[dangerclose]

so this TimesFM is also in the same category as TimeGPT from nixtlaverse?

[SpaceManNabs]

is there a ranking of the methods that actually work on benchmark datasets? Hybrid, "ML" or old stats? I remember eamonnkeogh doing this on r/ML a few years ago.