predict.artfima: 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

"predict"(object, n.ahead=10, ...)

Arguments

object

object of class "artfima"

n.ahead

number of steps ahead to forecast

...

optional arguments

Value

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

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.

Examples

Run this code

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)