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dlm (version 1.1-6.1)

dlmForecast: Prediction and simulation of future observations

Description

The function evaluates the expected value and variance of future observations and system states. It can also generate a sample from the distribution of future observations and system states.

Usage

dlmForecast(mod, nAhead = 1, method = c("plain", "svd"), sampleNew = FALSE)

Value

A list with components

amatrix of expected values of future states
Rlist of variances of future states
fmatrix of expected values of future observations
Qlist of variances of future observations
newStateslist of matrices containing the simulated future values
of the states. Each component of the list corresponds
to one simulation.
newObssame as newStates, but for the observations.

The last two components are not present if sampleNew=FALSE.

Arguments

mod

an object of class "dlm", or a list with components m0, C0, FF, V, GG, and W, defining the model and the parameters of the prior distribution. mod can also be an object of class "dlmFiltered", such as the output from dlmFilter.

nAhead

number of steps ahead for which a forecast is requested.

method

method="svd" uses singular value decomposition for the calculations. Currently, only method="plain" is implemented.

sampleNew

if sampleNew=n for an integer n, them a sample of size n from the forecast distribution of states and observables will be returned.

Author

Giovanni Petris GPetris@uark.edu

Examples

Run this code
## Comparing theoretical prediction intervals with sample quantiles
set.seed(353)
n <- 20; m <- 1; p <- 5
mod <- dlmModPoly() + dlmModSeas(4, dV=0)
W(mod) <- rwishart(2*p,p) * 1e-1
m0(mod) <- rnorm(p, sd=5)
C0(mod) <- diag(p) * 1e-1
new <- 100
fore <- dlmForecast(mod, nAhead=n, sampleNew=new)
ciTheory <- (outer(sapply(fore$Q, FUN=function(x) sqrt(diag(x))), qnorm(c(0.1,0.9))) +
             as.vector(t(fore$f)))
ciSample <- t(apply(array(unlist(fore$newObs), dim=c(n,m,new))[,1,], 1,
                    FUN=function(x) quantile(x, c(0.1,0.9))))
plot.ts(cbind(ciTheory,fore$f[,1]),plot.type="s", col=c("red","red","green"),ylab="y")
for (j in 1:2) lines(ciSample[,j], col="blue")
legend(2,-40,legend=c("forecast mean", "theoretical bounds", "Monte Carlo bounds"),
       col=c("green","red","blue"), lty=1, bty="n")

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