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The R package dlm implements filtering and smoothing (dlmFilter and dlmSmooth) for models with regression effects, but forecasting is not (yet) available for these models:

mod <- dlmModSeas(4)+dlmModReg(cbind(rnorm(100),rnorm(100)))
fi <- dlmFilter(rnorm(100),mod)
f <- dlmForecast(fi,nAhead=12)
Error in dlmForecast(fi, nAhead = 12): 
  dlmForecast only works with constant models

How can I do this in R?

Thanks for your help!

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Here's the solution I came up with: The trick is to add NAs to the end of the observation data. When seeing NA as a response variable the Kalman filter algorithm will simply predict the next value and not update the state vector. This is exactly what we want to make our forecast.

nAhead <- 12
mod <- dlmModSeas(4)+dlmModReg(cbind(rnorm(100+nAhead),rnorm(100+nAhead)))
fi <- dlmFilter(c(rnorm(100),rep(NA,nAhead)),mod)

Is this correct?

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  • $\begingroup$ Good idea, and it basically works. I suspect that there's a tradeoff of some sort -- otherwise the dlm folks could simply use that solution to code dlmForecast -- but it does basically work. $\endgroup$
    – Wayne
    Commented Sep 2, 2011 at 18:53
  • $\begingroup$ How do I get updated forecast for dlmModReg with new data? If I re-run dlmFilter, it will re-estimate my model parameters. $\endgroup$
    – user116555
    Commented May 23, 2016 at 8:16

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