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what's the consensus on machine learning vs more classical methods for time series forecasting? I know in 2018 a hybrid model won the M4 competition, obviously in this case classical still beats AI/ML

https://en.wikipedia.org/wiki/Makridakis_Competitions

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I think depends massively in what you mean by "time series". If it is really an ARMA model you're looking at then ML can only bring noise to the problem. If it is a complex large system that happens to be indexed by time, ML can well be better.

AFAIK Prophet had more modest scope than "be all and end all of TS modelling", rather a decent model for everything. It might indeed be excellent at that...

In the M5 competition[1], most winning solutions used LightGBM. So ML beat classical.

Just a couple of the winning solutions used DL.

[1] https://www.sciencedirect.com/science/article/pii/S016920702...

I would like to see the results of this ETS on the M5 Competition dataset, and see how fast it is compared to the ETS that was used as a benchmark. It goes without saying that accuracy is important, but reducing the total execution time is also pretty valuable.

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