呃，都贴进来吧，都用得上...

One-step forecasts without re-estimation

train <- window(hsales,end=1989.99)
fit <- auto.arima(train)
refit <- Arima(hsales, model=fit)
fc <- window(fitted(refit), start=1990)

Multi-step forecasts without re-estimation

h <- 5
train <- window(hsales,end=1989.99)
test <- window(hsales,start=1990)
n <- length(test) - h + 1
fit <- auto.arima(train)
fc <- ts(numeric(n), start=1990+(h-1)/12, freq=12)
for(i in 1:n)
{  
  x <- window(hsales, end=1989.99 + (i-1)/12)
  refit <- Arima(x, model=fit)
  fc <- forecast(refit, h=h)$mean[h]
}

Multi-step forecasts with re-estimation

h <- 5
train <- window(hsales,end=1989.99)
test <- window(hsales,start=1990)
n <- length(test) - h + 1
fit <- auto.arima(train)
order <- arimaorder(fit)
fcmat <- matrix(0, nrow=n, ncol=h)
for(i in 1:n)
{  
  x <- window(hsales, end=1989.99 + (i-1)/12)
  refit <- Arima(x, order=order[1:3], seasonal=order[4:6])
  fcmat[i,] <- forecast(refit, h=h)$mean
}
A variation on this also re-selects the model at each iteration. Then the second line in the loop is replaced with
refit <- auto.arima(x)