I'm curious as to how this would compare to having an actual statistician work on your data, because I feel that time series work is as much an art as it is a science. To start, selection of an appropriate timeframe is always important to ensure our data doesn't resemble either white noise or a random walk, and that we've given the response time of our data appropriate consideration! I find that people unfamiliar with statistics miss this point - I get people asking why I might use a weekly or biweekly timeframe for data when they reckon I should be using hourly or daily data. Selection of appropriate predictors is also important for multivariate time series and I have no idea how this model approaches that.

I also have questions about how interpretable the results outputted by this model are. With a more "traditional" model, I can easily look at polyroot or the [P/E]ACF, as well as various other diagnostic tools, and select a relatively simple model that results in a decent 95% prediction interval. I've always been very wary of black box models simply because I wouldn't be able to explain any findings derived from them well.

From skimming the blog post, is MAE all they're using for measuring the output quality?