1d:T7e2,# Predict method for artfima 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. ```r ## 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 ## Returns a list with two components - **Forecasts**: Description of 'comp1' - **SDForecasts**: Description of 'comp2' ## Author(s) A. I. McLeod, aimcleod@uwo.ca ## See Also `predict.Arima` ## 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 ```r 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) ```

predict.artfima function