# predict.Arima

##### Forecast from ARIMA fits

Forecast from models fitted by `arima`

.

- Keywords
- ts

##### Usage

```
## S3 method for class 'Arima':
predict(object, n.ahead = 1, newxreg = NULL,
se.fit = TRUE, \dots)
```

##### 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 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.

##### 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)
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*