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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