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That's correct. Since they don't change over time they're static and since you set the dtype to category LightGBM will treat them as such when training

On the static vs dynamic, it makes sense for something like "store_id" to be static because a store is a store. Repeating a static value is the only way to handle it. But for dynamic features (where my understanding of a dynamic feature is that it's something that varies over time or by date but is not related to the unique_id) wouldn't missingness and holidays be dynamic? A given date can be a holiday or not, and the date can change by year, or it might be missing or not. The convention isn't clear to me.

The definition is the following: • Static: same value for a single id across all timestamps • Dynamic: more than one unique value for a single id If you have dynamic features they can also be categorical, the only difference is that you have to provide them through

X_df

when using predict, whereas static features are just repeated automatically for you. Does that make sense?

So here, a holiday is dynamic, right? It can be 1 or 0, or many values

Yes

If that's the case, how should it be passed to training? I get "can't cast to {integer or float}" errors if I dont put it as a "static feature".

Do those errors come from the model?

I don't think so. It's lightgbm, so it's quite happy with categories. And if they're set as static it's fine, but if they're not then I get an error.

Can you paste the stacktrace of the error?

I'll reproduce it tomorrow

Thanks for the feedback. Were you not providing

X_df

at all or was it just missing that column?

not provided at all, which makes sense in hindsight but puzzled me for a while.

I think we can add some more errors in there, I'll work on that