Messiah? No. But one big plus that this article doesn't talk much about is how easy it is to get started with it even if you're a beginner.
A few lines of code gets you a fitted model and some insightful plots. From these you can see if 1) it's doing great and you don't need to spend hours training some crazy transformer model, 2) It's got some flaws and you should maybe try something else (which you can now compare against fbprophet as a baseline) or 3) This data is crazier than I thought, maybe we should rethink things...
TLDR: It's easy to throw this at a new forecasting problem, and although it isn't perfect (as the article shows) sometimes it is still a useful step IMO.
It's easy to get started yes and your point is taken WRT deep NN. In fairness, the first sentence in the article reads "Facebook's Prophet package aims to provide a simple, automated approach to prediction of a large number of different time series." However it's actually quicker to get started, one might argue, with other packages and its certainly quick to get an idea of which might be accurate:
https://microprediction.github.io/timeseries-elo-ratings/htm...
A few lines of code gets you a fitted model and some insightful plots. From these you can see if 1) it's doing great and you don't need to spend hours training some crazy transformer model, 2) It's got some flaws and you should maybe try something else (which you can now compare against fbprophet as a baseline) or 3) This data is crazier than I thought, maybe we should rethink things...
TLDR: It's easy to throw this at a new forecasting problem, and although it isn't perfect (as the article shows) sometimes it is still a useful step IMO.