Hi Spencer,
   
  I did go through the previous postings in the mailing list. But couldn't find satisfactory answer to my question. I am dealing with univariate time series. I suspect that my  data may contain some trend and seasonal components. Hence, rather than just fitting just AR(1) model, I am trying to find the right model which fits the data well and then use that model to forecast. In order to achieve this I am using best.arima model. If you have any other thoughts on this please let me know. 
   
  Thanx in advance for your help.
   
  Regards
  Sachin
   
  
Spencer Graves <spencer.graves@pdf.com> wrote:
  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
> 
> 
> 
> 
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