Hello, Were you able to find the slides from this video?

@@divugoel no I haven't. Still hoping someone with them will see this.

Arvid was kind enough to share his slides with us. Here is a link to the slides storage.googleapis.com/dockertest-191011/jc_temporal_fusion.html#1

I would think generally not. I mean that is your most general data science problem, how do you predict stuff that is wildly different from the past data that you have seen. Now, that doesn't mean that it isn't somewhat tracking, since the latest data does go in and most forecasts will be somewhat guided by the latest "is" state. The fact that you typically predict over many entities, you would also hope that some generalisation would naturally occur as some similarities have been already observed in other entities. Now these models are not truly causal, at least not out of the box. And they can't, because causality cannot be inferred from data (caveats caveats caveats). So it does fall back to the modeller to provide sensible covariates. As long as causal pathways don't break down (and sometimes that can happen, at least temporarily) the model generalises. If one only throws features at the model and hopes it finds all it needs by itself, one might be in for a bad surprise.

I'm using it to forcast 4 quarters ahead, having the present data only. It's working pretty well, I'm building my startup around this.

@@CalogeroZarbo how do you deal with data processing? Is it possible for you to share example code?

@@CalogeroZarbo hi, can you share some code (Python) ? Where is your startup web site? Thank you

@@carlosbahia5895 I think he is joking around