问题
I've written a function to iteratively forecast models built using the package dyn, and I'd like some feedback on it. Is there a better way to do this? Has someone written canonical "forecast" methods for the dyn class (or dynlm class), or am I venturing into uncharted territory here?
ipredict <-function(model, newdata, interval = "none",
level = 0.95, na.action = na.pass, weights = 1) {
P<-predict(model,newdata=newdata,interval=interval,
level=level,na.action=na.action,weights=weights)
for (i in seq(1,dim(newdata)[1])) {
if (is.na(newdata[i])) {
if (interval=="none") {
P[i]<-predict(model,newdata=newdata,interval=interval,
level=level,na.action=na.action,weights=weights)[i]
newdata[i]<-P[i]
}
else{
P[i,]<-predict(model,newdata=newdata,interval=interval,
level=level,na.action=na.action,weights=weights)[i,]
newdata[i]<-P[i,1]
}
}
}
P_end<-end(P)[1]*frequency(P)+(end(P)[2]-1) #Convert (time,period) to decimal time
P<-window(P,end=P_end-1*frequency(P)) #Drop last observation, which is NA
return(P)
}
Example usage:
library(dyn)
y<-arima.sim(model=list(ar=c(.9)),n=10) #Create AR(1) dependant variable
A<-rnorm(10) #Create independant variables
B<-rnorm(10)
C<-rnorm(10)
Error<-rnorm(10)
y<-y+.5*A+.2*B-.3*C+.1*Error #Add relationship to independant variables
data=cbind(y,A,B,C)
#Fit linear model
model.dyn<-dyn$lm(y~A+B+C+lag(y,-1),data=data)
summary(model.dyn)
#Forecast linear model
A<-c(A,rnorm(5))
B<-c(B,rnorm(5))
C<-c(C,rnorm(5))
y=window(y,end=end(y)+c(5,0),extend=TRUE)
newdata<-cbind(y,A,B,C)
P1<-ipredict(model.dyn,newdata)
P2<-ipredict(model.dyn,newdata,interval="prediction")
#Plot
plot(y)
lines(P1,col=2)
回答1:
predict.Arima
in the core of R has the n.ahead
argument to forecast n
steps ahead and it seems that that is what you are looking for in conjunction with dyn but predict.dyn
does not currently support that functionality. To get that effect one must iteratively call dyn$whatever
as you are doing.
来源:https://stackoverflow.com/questions/4856555/iteratively-forecasting-dyn-models