predict.artfima {artfima} R Documentation

Predict method for artfima

Description

The optimal minimum mean square error forecast and its standard deviation for lags 1, 2, ..., n.ahead is computed at forecast origin starting at the end of the observed series used in fitting. The exact algorithm discussed in McLeod, Yu and Krougly is used.

Usage

## S3 method for class 'artfima'
predict(object, n.ahead=10, ...)

Arguments

 object object of class "artfima" n.ahead number of steps ahead to forecast ... optional arguments

Value

a list with two components

 Forecasts Description of 'comp1' SDForecasts Description of 'comp2'

Author(s)

A. I. McLeod, aimcleod@uwo.ca

References

McLeod, A.I., Yu, Hao and Krougly, Z. (2007). Algorithms for Linear Time Series Analysis: With R Package. Journal of Statistical Software 23/5 1-26.

predict.Arima

Examples

ans <- artfima(seriesa, likAlg="Whittle")
predict(ans)
#compare forecasts from ARTFIMA etc.
## Not run:
ML <- 10
ans <- artfima(seriesa)
Ftfd <- predict(ans, n.ahead=10)$Forecasts ans <- artfima(seriesa, glp="ARIMA", arimaOrder=c(1,0,1)) Farma11 <- predict(ans, n.ahead=10)$Forecasts
ans <- artfima(seriesa, glp="ARFIMA")
Ffd <- predict(ans, n.ahead=10)$Forecasts #arima(0,1,1) ans <- arima(seriesa, order=c(0,1,1)) fEWMA <- predict(ans, n.ahead=10)$pred
yobs<-seriesa[188:197]
xobs<-188:197
y <- matrix(c(yobs,Ffd,Ftfd,Farma11,fEWMA), ncol=5)
colnames(y)<-c("obs", "FD", "TFD", "ARMA11","FEWMA")
x <- 197+1:ML
x <- matrix(c(xobs, rep(x, 4)), ncol=5)
plot(x, y, type="n", col=c("black", "red", "blue", "magenta"),
xlab="t", ylab=expression(z[t]))
x <- 197+1:ML
points(xobs, yobs, type="o", col="black")
points(x, Ffd, type="o", col="red")
points(x, Ftfd, type="o", col="blue")
points(x, Farma11, type="o", col="brown")
points(x, fEWMA, type="o", col="magenta")
legend(200, 18.1, legend=c("observed", "EWMA", "FD", "TFD", "ARMA"),
col=c("black", "magenta", "red", "blue", "brown"),
lty=c(rep(1,5)))

## End(Not run)


[Package artfima version 1.5 Index]