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.

Reply via email to