# predict.Arima

0th

Percentile

##### Forecast from ARIMA fits

Forecast from models fitted by arima.

Keywords
ts
##### Usage
"predict"(object, n.ahead = 1, newxreg = NULL, se.fit = TRUE, ...)
##### Arguments
object
The result of an arima fit.
The number of steps ahead for which prediction is required.
newxreg
New values of xreg to be used for prediction. Must have at least n.ahead rows.
se.fit
Logical: should standard errors of prediction be returned?
...
arguments passed to or from other methods.
##### Details

Finite-history prediction is used, via KalmanForecast. This is only statistically efficient if the MA part of the fit is invertible, so predict.Arima will give a warning for non-invertible MA models.

The standard errors of prediction exclude the uncertainty in the estimation of the ARMA model and the regression coefficients. According to Harvey (1993, pp.\ifelse{latex}{\out{~}}{ } 58--9) the effect is small.

##### Value

A time series of predictions, or if se.fit = TRUE, a list with components pred, the predictions, and se, the estimated standard errors. Both components are time series.

##### References

Durbin, J. and Koopman, S. J. (2001) Time Series Analysis by State Space Methods. Oxford University Press.

Harvey, A. C. and McKenzie, C. R. (1982) Algorithm AS182. An algorithm for finite sample prediction from ARIMA processes. Applied Statistics 31, 180--187.

Harvey, A. C. (1993) Time Series Models, 2nd Edition, Harvester Wheatsheaf, sections 3.3 and 4.4.

arima
library(stats) od <- options(digits = 5) # avoid too much spurious accuracy predict(arima(lh, order = c(3,0,0)), n.ahead = 12) (fit <- arima(USAccDeaths, order = c(0,1,1), seasonal = list(order = c(0,1,1)))) predict(fit, n.ahead = 6) options(od)