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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