Recently I saw that Facebook released Neural Prophet, a new forecasting package similar to Prophet, but built on top of Torch. Prophet is one of my favorite forecasting packages, given the ability to decompose forecasts, add in events and holidays, and take advantage of business user domain knowledge. Naturally, I was excited about hearing this new version, and on top of torch of all things! The package itself is early in development, so there’s obviously no R port yet.
The only constants in life are death, taxes, and the RStudio team continually crushing it. This time, they’ve ported Torch into R. I’m a fairly heavy tensorflow user, and coming from an R background had a steep learning curve incorporating it into my toolkit. While torch is simpler in a lot of ways (specifically, not requiring a python environment), these deep learning frameworks can be intimidating. What I hope to do here is demystify torch workflows a little bit by providing some overly simple use cases.
