library(ts)
data(UKDriverDeaths)
plot(UKDriverDeaths)
diff.1 <- diff(UKDriverDeaths,12)
plot(diff.1)
par(mfrow=c(1,2))
acf(diff.1,lag.max=40)
pacf(diff.1,lag.max=40)
seatbelt <- as.matrix(c(rep(0,169),rep(1,23)))
fit <- arima0(UKDriverDeaths,order=c(1,0,1),seasonal=list(order=c(0,1,1),period=12), xreg=seatbelt)
print(fit)
fcast <- predict(fit,n.ahead=12,newxreg=as.matrix(rep(1,12)))
plot(UKDriverDeaths,xlim=c(1969,1986),ylim=c(900,2600))
lines(fcast$pred,col=2)
lines(fcast$pred + 2*fcast$se,col=3)
lines(fcast$pred - 2*fcast$se,col=3)