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 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
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
Spencer Graves 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
Hi, Kjetil: Thanks. Spencer Graves
Kjetil Brinchmann Halvorsen wrote:
Spencer Graves 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
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
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
