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!
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