# predict.Arima

##### Forecast from ARIMA fits

Forecast from models fitted by `arima`

.

- Keywords
- ts

##### Usage

```
# S3 method for Arima
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 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.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 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.

##### See Also

##### Examples

`library(stats)`

```
# 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))))
predict(fit, n.ahead = 6)
options(od)
# }
```

*Documentation reproduced from package stats, version 3.6.0, License: Part of R 3.6.0*