Forecasting package in R

December 8, 2013

(This article was first published on Copula, and kindly contributed to R-bloggers)

Probably the last post before year end and very typical for me is to review this years system performance. A couple of R-packages are since 1 year the backbone of my lab. Just recently I added a package called “Forecast” which is a brilliant implementation of a couple of well known models. It is well explained in a free book accompanying this package and explaining the math in detail.
To understand the differences of the forecast models better I wrote some code to backtest on the DAX and the DAX ETF (EXS1.DE) with some Parameters.
backval <- seq(20,50,by=5)
aheadval <-seq(10,10,by=5)
para <- expand.grid(back=backval,ahead=aheadval,
#models <- list(m1=hw,m2=holt,m3=ses,m4=forecast,m5=naive,m6=meanf,m7=thetaf)
models <- list(m1=ets,m2=auto.arima,m3=hw,m4=holt,m5=thetaf,m6=naive)

stat <- matrix(nrow=nrow(para)*10+length(models),ncol=10)
colnames(stat)=c(“z”, “model”,”para”,”match”, “long”,”profit”,”winner”,”loser”,”limit exit”,”limit wrong”)

I’ve decided to take a history of up to 50 days(observations), which might be inappropriate for auto.arima which is not only a call to a model as the name suggests auto.arima returns also the optimal parameters found for the given period. That’s why the code appears to be slow but there is a lot going on and it is definitely faster as my R-optimizer I used for finding the best model AIC as it is implemented in C++.

Once you have the xts object you can loop like that

 xvars <- x[posab:posbis,c(4)]
      for(y in 1:length(models)){
        #c= models[[y]](xvars,h=ahead,level=confidence)
        #co=try(correlationTest(x[sfcab:sfcbis,4], c$mean[1:ahead], “pearson”), silent=TRUE)

 xvars will hold the back period and ahead is the prediction period. 

I found the ets and the holt model as pretty good predictors but I’m still investigating.

That was it from me for 2013 have a merry Christmas and a wonderful 2014.

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