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We hope this exercise helps the forecast community avoid adopting yet another overpromising and unproven forecasting method.
Exponential Smoothing: faster and more accurate than NeuralProphet
github.com
I'd need to dig out the original paper, but I would be surprised if the original didn't compare to basic benchmark methods. But from memory, I never saw such a comparison (until now).
ML (particularly DL) tends to outperform "classical" statistical time series forecasting when the data is (strongly) nonlinear, highly dimensional and large. The opposite holds as well.
It is also important to note that accuracy is not the only relevant metric in practical applications. Explainability is of particular interest in time series forecasting: it is good to know if your sales are going to increase/decrease, but it is even more valuable to know which input variables are likely to account for that change. Hence, a "simple" model with inferior forecasting accuracy might be preferred to a stronger estimator if it can give insights to not only the "what" will happen, but also the "why".
As usual in ML, the appropriate solution depends on the problem and context. ML (particularly DL) tends to outperform "classical" statistical time series forecasting when the data is (strongly) nonlinear, highly dimensional and large. The opposite holds as well. It is also important to note that accuracy is not the only relevant metric in practical applications. Explainability is of particular interest in time series…
This claim about forecasting with DL comes up a lot, but I’ve seen little evidence to back it up.
Personally, I’ve never managed to have the same success others apparently have with DL time series forecasting.
Presumably this apparent non-transitivity is because of differences in each evaluation. If we fix the evaluation to the method used here, is it still the case that NeuralProphet outperforms Prophet (and therefore the claim that Prophet outperforms ETS is not correct)? Or is it that NeuralProphet does not outperform Prophet, but Prophet does outperform ETS?
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
Just a couple of the winning solutions used DL.
[1] https://www.sciencedirect.com/science/article/pii/S016920702...
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
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...