Is Facebook's “Prophet” the time-series Messiah or just a naughty boy?

For modern Deep Learning based probabilistic forecasting you can try DeepAR with parametric likelihood function [0] or Multi-Horizon Quantile RNN (non-parametric) [1]. The implementations of these models in Pytorch and MXnet are scattered all over the place.

[0] https://arxiv.org/abs/1704.04110

[1] https://arxiv.org/abs/1711.11053

EDIT: formatting

In my experience, the time-series models that are reliably predictive typically aren't purely autoregressive but contain exogeneous variables as well (i.e. yₖ = f(yₖ₋₁, yₖ₋₂, ..., xₖ, xₖ₋₁, xₖ₋₂...), like ARX models). These models don't only capture relationships to historical patterns but to other driving/causal variables.

Price forecasts are often modeled as time-series models, but this assumes that prices only have time-based patterns which is often not true. In my domains of interest for instance, time has tangible yet limited effect on prices -- prices are driven more by variables like weather and certain types of market activity.

Prophet solves a broad class of easy problems that a lot of ordinary businesses have: you have several years of basically regular data (sales or page views or store foot traffic) that you know has yearly/weekly/daily (if you have sub-daily data) cycles, and you want to give a reasonable prediction to the business so they can plan for the upcoming week/month/year. And you want to remove the periodic effects so you can see the underlying trends.

Imagine someone, lets call them Bill, who might be called a data scientist, or business analyst or just assistant operations manager, for a medium-large business. Bill has the last 5 years of sales/views/traffic data in the database (anything before that is in a bunch of excel spreadsheets on the share drive), and knows just enough python to be dangerous. Bill can probably explain an R-squared value but is not an expert at statistics by any measure. He wants to fit the data, but has several problems:

1) the weekly trend does not line up with the yearly data, as the year starts on a different weekday.

2) Those damn public holidays, some of them occur on a specific date, some of them on the "first Monday of the month", and some of them seem to change almost randomly year-to-year.

3) The reporting system was down for a couple of weeks in June and Feb last year, and the numbers for the first few years were copied from excel, so sometimes are missing the first or last day of the month.

Prophet comes by default with yearly/weekly seasonality. Prophet comes out-of-the-box with a simple way to import holidays, and even a way to specify your own. Prophet doesn't require any cleaning, or special procedures to deal with missing data. And it is quick and easy to use, and get nice-looking, broadly reasonable graphs out (with the above mentioned, consistent data). And that solves the business problem.

And Bill's probably heard of it because it is (a) popular already, and (b) has Facebook's name attached.

That's my take as to why, even if it is not even close to the most accurate method, Prophet is so broadly popular.

[1] https://news.ycombinator.com/item?id=27697274

[0] https://twimlai.com/causal-models-in-practice-at-lyft-with-s...