Fitted Confidence Intervals

I am using the forecast function in R written by Rob Hyndman. I am trying to determine the best way to determine confidence intervals for the historical fitted values. I would like to plot how often the historical forecast exceeded the 95% confidence interval at that time.

Presumably you mean prediction intervals rather than confidence intervals.

The fitted values are in-sample one-step forecasts. Assuming normally distributed errors, 95% prediction intervals are given by

           yt±1.96σ^

where σ^2 is the estimated variance of the residuals.

Here is an example using R:

library(forecast)
fit <- auto.arima(Nile)
upper <- fitted(fit) + 1.96*sqrt(fit$sigma2)
lower <- fitted(fit) - 1.96*sqrt(fit$sigma2)
plot(Nile, type="n", ylim=range(lower,upper))
polygon(c(time(Nile),rev(time(Nile))), c(upper,rev(lower)), 
   col=rgb(0,0,0.6,0.2), border=FALSE)

lines(Nile)
lines(fitted(fit),col='red')

out <- (Nile < lower | Nile > upper)
points(time(Nile)[out], Nile[out], pch=19)