It’s not really predicting “egg prices” or “inflation” — it’s mostly fitting patterns that happen to show up in those series.

The problem isn’t domain generalization, it’s that we keep pretending these models have any notion of what the data means.

People ask how one model can understand everything, but that assumes there’s any understanding involved at all.

At some point you have to ask: how much of “forecasting” is actually anything more than curve fitting with better marketing?

"curve-fitting" has a long history (centuries old) and could be regarded more as a numerical method issue.

Rigorous understanding of what is over fitting, techniques to avoid it and select the right complexity of the model, etc, are much newer. This is a statistical issue.

My point is that forecasting isn't curve fitting, even thought curve fitting is one element of it.