I've fit a Dyanmic Linear Model to some data using the dlmFilter in R [from the dlm package]. From said filter I have predicted 7 steps ahead using the dlmForecast function. The predicted outcome is very good, but I would like to add a 95% confidence interval and [after a lot of testing] have struggled to do so.
I've mocked up some similar code, below:
library(dlm)
data <- c(20.68502, 17.28549, 12.18363, 13.53479, 15.38779, 16.14770, 20.17536, 43.39321, 42.91027, 49.41402, 59.22262, 55.42043)
mod.build <- function(par) {
dlmModPoly(1, dV = exp(par[1]), dW = exp(par[2]))
}
# Returns most likely estimate of relevant values for parameters
mle <- dlmMLE(a2, rep(0,2), mod.build); #nileMLE$conv
if(mle$convergence==0) print("converged") else print("did not converge")
mod1 <- dlmModPoly(dV = v, dW = c(0, w))
mod1Filt <- dlmFilter(a1, mod1)
fut1 <- dlmForecast(mod1Filt, n = 7)
The forecast outcome appears to be very good [although the model to some extent over-fits the data due to the small number of observations]. However, I would like to add a 95% confidence interval and have struggled to figure out how to do so.
Any advice would be appreciated?
Cheers
