I was under the impression that FB Prophet was optimal for significantly seasonal time series data.
Honestly given the fickle nature of these kind of growth patterns beyond the very near term, an ARIMA with a flat vol or a simple eyeball extrapolation in my experience as a quant would likely generate just as reasonable/reliable results.
While I understand this is likely intended as a standalone project, it would be interesting to run a comparison of ARIMA vs FB Prophet on out of sample trending Github tools/file types, as well as the general performance of these predictions beyond a one year time frame (especially vs the reported confidence intervals in Prophet).
I am not that familiar with how Prophet works, so I am absolutely open to being humbled and corrected. I have a project myself that has a varying seasonal component and I am looking forward to diving into Prophet for a deeper understanding. I am attempting to model an Asian 2 asset spread option with a volume weighted average index price setting mechanism where the underlying exhibits seasonality in the volume traded over the trading time window. I am currently running a Monte Carlo on the valuation with a simple average settlement assumption, as opposed to a volume weighted average assumption, and I was thinking Prophet could help.
Does anyone have experience in financial time series analysis and option valuation who would care to chime in?
Also, what is everyone's thoughts on using prophet non seasonal vol clustering times series?
I will likely publish something on my project using stale data given I work in a trading environment. The theory should be the same though. One of these days I’d like to actually write a solid white paper level research study and get published! One can dream!