Re: [R] AICc vs AIC for model selection

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Tue 18 Jul 2006 - 01:37:32 EST

          I understand that you have only 26 observations. Model identification always requires more observations than estimating a model you already think you know. If it were my problem, I think I'd first plot the data over time and make a normal probability plot of the data.   Then I'd fit the simplest, most parsimonious model I could think of that would include the trend and seasonal. Then I'd examine the residuals and check the p values. If everything looked sensible, wouldn't push it further. If I had several such series, I'd study Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer) and use the 'nlme' package to do more.

	  Hope this helps.
	  Spencer Graves

Sachin J wrote:
> 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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