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by microprediction 1818 days ago
To close, let me say that this post ended up being more negative than I expected and, like Nessie, my opinion may rise in the future when I understand the implications of the Prophet generative model better, and either modify it or find better ways to identify its strengths. The unanswered question here is why Prophet is so popular, and this surely merits a better explanation than I have given. I think there are probably statistical angles I am not seeing - something reflecting the fact that people are voting with their eyeballs when they use Prophet.

(from the article)

- The author

2 comments

> The unanswered question here is why Prophet is so popular, and this surely merits a better explanation than I have given.

It has been explained by several posters. The goal of Prophet is to make time series accessible to non-experts, and the alternatives to Prophet are significantly more complicated. I say this as someone who has done pretty extensive work with time series for BigCorp's chain of retail stores.

A moving average is not complicated. A moving average is more accurate. So I don't believe it has been explained in this thread at all.
The title is a bit strong too, but maybe that’s what you were going for.
Stronger than "Prophet"?

I began writing this post because I was working on integrating Prophet into a Python package I call time machines, which is my attempt to remove some ceremony from the use of forecasting packages and compare them. These power some bots that the prediction network (explained at www.microprediction.com if you are interested). How could I not include the most popular time series package?

I hope you interpret this post as nothing more than an attempt to understand the quizzical performance results, without denying the possible utility of Prophet or its strengths (if nothing else it might be classified as a change-point detection package). I mean seriously, can Prophet really be all that bad? At minimum, all those who downloaded Prophet are casting a vote for interpretability, scalability and good documentation - but perhaps accuracy as well in a manner that is hard to grasp quantitatively.

(from the article)