stats (version 3.3.1)

# predict.Arima: Forecast from ARIMA fits

## Description

Forecast from models fitted by `arima`.

## 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.

## 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.

## 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.

## 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`

## Examples

Run this code
``````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))))