You don’t even have to limit it to machine learning, the definition of forecasting is isomorphic to the definition of modeling, which, with the dilution of the term AI, is also isomorphic to the definition of AI.

More simply:

  - forecasting = modeling = AI

Edit: I’d even throw statistics into that extended equality, meaning that Bayes, Bernoulli and even the fellow named John Gaunt have a strong case for having invented AI.

For statistical time series forecasting, yes. This is for judgment-based forecasting, a somewhat different problem. It often involves, e.g. estimating the probabilities of one-off future events, which time series forecasting models aren’t suited for.

Yes, and if the things I learned in my university class on the subject still holds, forecasts are incredibly sensitive to modeling decisions such as what independent variables you choose and how you believe they might mathematically relate to the outcome variable. It’s not a zero skill thing, but if anyone’s found a way to consistently mitigate the luck factor then I’d expect them to be wealthier than Elon Musk by now.

And there’s always a huge amount of variation that you simply can’t model, for whatever reason, and is therefore functionally a random factor.

I don’t want to say too much because this isn’t something I went on to actually do after school so I’m way out of my lane here, but I can see room for this to be more akin to “AI wins parcheesi tournament” than it is to “AI wins chess tournament.”

I think also a lot of it is intuition.