But then, I've read a couple of posts discussing in depth how prophet itself was not always the go-to model [1], so perhaps some caution is in order.
[1] https://www.microprediction.com/blog/prophet
[edit] And this one, linked in [1]: https://kourentzes.com/forecasting/2017/07/29/benchmarking-f...
Prophet has a relatively narrow use case where it makes sense; not coincidentally, it makes sense at a place like Facebook where the sheer variety of problems to be tackled cannot scale to such a foregoing team for each such problem.
Prophet has received a lot of backlash for workflows it never intended, nor claimed, to be able to tackle. That's undeserved in my opinion. Zillow didn't lose money because it used Prophet, for example. But it still really shouldn't be used as a benchmark like this.
We agree that in most cases prophet is not a good benchmark; however, we wanted to use it because it is one of the most used libraries in forecasting. For that reason, we also tested the solution against AWS Forecast obtaining better results.
Besides the better performance and scalability, the pipeline we created considering all the stages of time series forecasting: preprocessing (e.g. missing value imputation), creation of static and dynamic features, forecast generation, and finally evaluation using data sets of important competencies. (https://github.com/Nixtla/tsfeatures)
On the deployment side, the entire pipeline can be quickly deployed in the user's cloud using terraform. This allows for less development time. (https://github.com/Nixtla/nixtla)