compared to using non-GP as, e.g., S_cumsum ~ log2(week) with this result: gamma-poisson.pdf (18.5 KB)

For some reason, in the GP case the EA curve goes down even though I’m dealing with cumulative data. I presume it is that at the end of the dataset EA plateaus:

I have two techniques being compared on a weekly basis: wekly data.csv (492 Bytes)

Making the columns cumsum() makes it easy to compare against a third approach which is a linear approach y=343x. I would like to model the techniques for the 54 weeks I have, but then make forecasts for 200+ weeks if possible (well it’s always possible, but I’d like to see if it is useful).

Modeling it with the outcome as cumsum() and the predictor as log2(week) using negbinomial() makes sense (see below fig), but when I try simple GP approaches they seem to have much better out of sample prediction so that makes me curious… Rplot.pdf (18.5 KB)

Hmm, more like … model GP so that it must be monotonic

Like… if you put GP on a difference… or basically on a derivative and then construct you cumsum from that. If you model derivative so its larger than 0, then the main signal should be monotonic.

Sure, I don’t have any specific model at hand now and yes it could fail.