Regarding AIC.c, have you tried RSiteSearch("AICc") and RSiteSearch("AIC.c")? This produced several comments that looked to me like they might help answer your question. Beyond that, I've never heard of the "forecast" package, and I got zero hits for RSiteSearch("best.arima"), so I can't comment directly on your question.
Do you have only one series or multiple? If you have only one, I think it would be hard to justify more than a simple AR(1) model. Almost anything else would likely be overfitting. If you have multiple series, have you considered using 'lme' in the 'nlme' package? Are you familiar with Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer)? If not, I encourage you to spend some quality time with this book. My study of it has been amply rewarded, and I believe yours will likely also. Best Wishes, Spencer Graves Sachin J wrote: > Hi, > > I am using 'best.arima' function from forecast package to obtain point forecast for a time series data set. The documentation says it utilizes AIC value to select best ARIMA model. But in my case the sample size very small - 26 observations (demand data). Is it the right to use AIC value for model selection in this case. Should I use AICc instead of AIC. If so how can I modify best.arima function to change the selection creteria? Any pointers would be of great help. > > Thanx in advance. > > Sachin > > > > > --------------------------------- > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html