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I found this comment from their paper especially true of our times:

Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers. We argue that this distinction does not stem from fundamental differences in the methods assigned to either class. Instead, this distinction is probably of a tribal nature, which limits the insights into the appropriateness and effectiveness of different forecasting methods.

... if there were ever just one word to describe the "DL vs ML vs Stats" debate in forecasting, tribal has got to be it lol maybe i'm missing something but afaict this only really matters to academics. in practice the recipe seems actually very straight forward: • try a bunch of fast + cheap shit, see what works • try a bunch of slightly less fast + cheap shit, see what works • repeat until you're happy or out of time / money ... i'd also love to meet the sucker who spent 11k training a DL forecaster 🤣 we're down to ~10-15 cents a pop!