Hello everybody!
I have an ARIMA model for a time series. This model was obtained through an
auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with
drift (my time series has monthly data). Then I perform a 12-step ahead
forecast to the cited model... so far so good... but when I look the plot of
my forecast I see that the result is really far from the behavior of my time
series... in fact, there is a considerable gar between the last value of
the series and the first forecast. My guess is that I'm doing something
wrong. Here is what I do:
>mods<-auto.arima(x[[1]],start.p=0,start.q=0,start.P=0,start.Q=0,stepwise=TRUE,stationary=FALSE)
>ARIMA(2,1,4)(2,0,1)[12] with drift # the output
Call: auto.arima(x = x[[k]], start.p = 0, start.q = 0, start.P = 0, start.Q
= 0, stationary = FALSE, stepwise = TRUE)
Coefficients:
ar1 ar2 ma1 ma2 ma3 ma4 sar1
0.0639 -0.7820 -1.2103 1.2236 -0.9511 0.2357 1.0031
s.e. 0.0686 0.0582 0.1098 0.1558 0.1568 0.1007 0.0716
sar2 sma1 drift
-0.0711 -0.8963 -780.9456
s.e. 0.0747 0.0608 403.2112
sigma^2 estimated as 10202381: log likelihood = -1100.61
AIC = 2206.69 AICc = 2209.23 BIC = 2236.98
>for<-forecast(mods,h=12,newxreg=(1+length(x[[1]])):(length(x[[1]]+12)))
#forecast
and as I said before, the results dont seem to be right. In fact, when I
restrict the search of the model on the auto.arima function to stationary
models only an I perform the forecast (without the newxreg-option) the
results are very much acceptable.
ANY HELP OR COMMENTARY I VERY WELCOMED!!!!!! thanks in advance!
Diego.
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