Good afternoon! I'm trying to model a ts but unfortunately i'.m very new to this kind of modeling so i 'll be very grateful if you have an advice.
This was my syntax: t<-c(16115,17391,19011,20256,19034,18851,20016,18088,19166,21163,18463,19397,15800,16113,18879,20598,17252,19753,19110,19605,20836,18868,20204,24384,15817,18223,19884,21059,18545,19853,20027,20061,21679,20210,20351,21322,16891,17111,20166,18735,16821,17891,17058,19250) plot.ts(t) acf(t) pacf(t) arima(t,order=c(13,2,0), seasonal=list(order=c(1,1,1)))->fit predict(fit, n.ahead=1) Now, choosing my order, was a trial and error process where i used the acf, pcf and AIC (which is minimized by taking those specific values) but as you can imagine this is something "made by ear". I want to know if there is some test which can give me the proper values for the order? (i haven't found some full examples which describe the process fully from head to toe) Second, if this is the best fitting model for that specific ts, now don't you think that those standard errors are huge!!!!!!!!!!!!! Say, if the novice client eliminates that great rise/damp in the series and wants to "predict" solely based on his impressions from the time series he would probably give me the same interval, as the arima gave me, and i can't give him some new piece of info. Is there something wrong with my ts, or with my arima model, and how could i make that confidence interval smaller ? Thank you and have a great day! --------------------------------- [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.