predict.Arima
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. - n.ahead
- The number of steps ahead for which prediction is required.
- newxreg
- New values of
xreg
to be used for prediction. Must have at leastn.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.
See Also
Examples
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)