Forecast probabilities for (non) homogeneous MSAR models

Description

Computes, for each time t, the conditional probabilities for MSAR models P(Y_t|y_{1:(t-1)}) where Y is the observed process and y the observed time series.

Usage

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forecast.prob.MSAR(data, theta, yrange = NULL, covar.emis = NULL, covar.trans = NULL)

Arguments

data

observed time series, array of dimension T*N.samples*d

theta

object of class MSAR including the model's parameter and description. See init.theta.MSAR for more details.

yrange

values at which to compute the forecast probabilities

covar.emis

emission covariate if any.

covar.trans

array of univariate or multivariate series of covariate to take into account in the transition probabilities. The link function is defined in the initialisation step (running init.theta.MSAR.R).

Value

A list containing

..$yrange

abscissa for the forecast probabilities

..$prob

forecast probabilities

Yhat

forecasted value

Author(s)

Valerie Monbet, valerie.monbet@univ-rennes1.fr

See Also

prediction.MSAR

Examples

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## Not run
#data(meteo.data)
#data = array(meteo.data$temperature,c(31,41,1)) 
#T = dim(data)[1]
#N.samples = dim(data)[2]
#d = dim(data)[3]
#M = 2
#theta.init = init.theta.MSAR(data,M=M,order=2,label="HH")
#res.hh.2 = fit.MSAR(data,theta.init,verbose=TRUE,MaxIter=200)
#FP = forecast.prob.MSAR(data,res.hh.2$theta)
#plot(data[,1,],typ="l")
#lines(FP$Yhat[,1],col="red")
#alpha = .1
#IC.emp = matrix(0,2,T)
#for (k in 1:length(data[,1,])) {
#	tmp = cumsum(FP$prob[,k,1])/sum(FP$prob[,k,1])
#	IC.emp[1,k] = FP$yrange[max(which(tmp<alpha/2))]
#	IC.emp[2,k] = FP$yrange[max(which(tmp<(1-alpha/2)))]
#}
#lines(IC.emp[1,],lty=2,col="red")
#lines(IC.emp[2,],lty=2,col="red")

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