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by tech_ken
992 days ago
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Two explanations First: Prophet is not actually "one model", it's closer to a non-parametric approach than just a single model type. This adds a lot of flexibility on the class of problems it can handle. With that said, Prophet is "flexible" not "universal". A time series of entirely random integers selected from range(0,10) will be handled quite poorly, but fortunately nobody cares about modeling this case. Second: the same reason that only a small handful of possible stats/ML models get used on virtually all problems. Most problems which people solve with stats/ML share a number of common features which makes it appropriate to use the same model on them (the model's "assumptions"). Applications which don't have these features get treated as edge-cases and ignored, or you write a paper introducing a new type of model to handle it. Consider any ARIMA-type time series model. These are used all the time for many different problem spaces, and are going to do reasonably well on "most" "common" stochastic processes you encounter in "nature", because its constructed to resemble many types of natural processes. It's possible (trivial, even) to conceive of a stochastic process which ARIMA can't really handle (any non-stationary process will work), but in practice most things that ARIMA utterly fails for are not very interesting to model or we have models that work better for that case. |
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