[melondonkey]

As a practitioner the most impactful library for time series has been brms, which basically gives you syntactic sugar for creating statistical models in Stan. Checks all the boxes including probabilistic forecasts, multiple link functions for the likelihood including weiner, gamma, Gaussian, student t, binomial, zero-inflated and hurdle models. Also has auto-regressive and ordinal predictors and you actually learn something from your data.

I find a lot of these ML and DL libraries to be harder to troubleshoot beyond blind hyperparameter tuning whereas with stats I can tweak model, modify likelihood, etc. There’s also a lot of high value problems that have few data points these libraries tend to want at least daily data.

[bethekind]

Could you expand on what you mean by "practitioner?"

Also a followup question. With timeGPT and chronos advertised as "foundational time series models", do you think they have any value?

[melondonkey]

I guess I just mean I’m a data scientist—someone who uses models like these in practice as opposed to someone who develops them.

I’m not sure what to even make of a term like “foundational time series”. Does that just mean it’s widely used and known? You have to earn a role like that you can’t just declare yourself one.