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I got really excited for a second. Actually, I'm still pretty excited about this even if it was something else entirely.
So...How much will this do at forecasting stock prices? =) Very cool though --- I would be interested to dive into the methods they've implemented sometime in the near future!
Some feedback: it'd be nice to see you actually quantify how accurate Prophet's forecasts are on the landing page for the project. In the Wikipedia page view example, you go as far as showing a Prophet forecast, but it'd be nice to have you take it one step further and quantify its performance. Maybe withhold some of the data you use to fit the model and see how it performs on that out of sample data. It's nice that you show qualitatively that it captures seasonality, but you make bold claims about its accuracy and the data to back those claims up is conspicuously absent. Related, it might be worth benchmarking its performance against existing automated forecasting tools.
I'll definitely be checking it out!
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... and if you like Clojure, you might try Bayadera, which has its own engine running the analysis on the GPU. http://github.com/uncomplicate/bayadera
This looks like it could be awesome but it has almost no information about what its purpose is or how to use it.
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This looks like it could be awesome but it has almost no information about what its purpose is or how to use it.
You are right. The docs have been due to be written for many months now, and that is the main reason the library has not been released yet. On the other hand, the test folder contains many tests, among them full examples from many chapters from the book Doing Bayesian Dara Analysis, recommended above.
And I wait with bated breath.
Worth noting Prophet is R/Python wrappers to some models with reasonable defaults, written in and fit by Stan , a probabilistic programming language, and Bayesian estimation framework. Stan is amazing in that you can fit pretty much any model you can describe in an equation (given enough time and compute, of course)! More on Stan here: http://mc-stan.org/
... and if you like Clojure, you might try Bayadera, which has its own engine running the analysis on the GPU. http://github.com/uncomplicate/bayadera
Cloned and tried to build it but I'm getting an error regarding uncomplicate:commons:0.3.0-SNAPSHOT being unavailable on clojars. Is that something you currently have installed to your local maven repo? I don't see it here: https://clojars.org/repo/uncomplicate/commons/
I can get it to build with 0.2.2 but it is missing the "releaseable?" function.
In any case this looks awesome and I'll be keeping an eye on it / playing with it for some new projects.
EDIT: I was able to get it building by cloning your commons library and running "lein install". :)
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Do you know of any good beginner tutorials for Stan or probabilistic programming in general? All the examples that I found seemed quite complex and I was a bit overwhelmed by all the math. Which might also be a sign that I should brush up my math skills. What kind of math/stats should I revise to be able to better understand probabilistic programming?
Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan. It is very approachable and also has lots of practice problems. It's not a math-heavy book at all. Edit: I wouldn't recommend Probabilistic Programming and Bayesian Methods for Hackers. When I tried using it, I felt that too much was glossed over. The book that I recommend excels at conveying a strong intuition for how these various tech…
Free is hard to beat, especially for someone just testing the waters.