stats (version 3.6.2)

# predict.Arima: Forecast from ARIMA fits

## Description

Forecast from models fitted by `arima`.

## Usage

```# S3 method for Arima
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.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 AS 182: An algorithm for finite sample prediction from ARIMA processes. Applied Statistics, 31, 180--187. 10.2307/2347987.

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

`arima`

## Examples

Run this code
``````# NOT RUN {
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))))