Returns forecasts and other information for univariate ARIMA models.

```
# S3 method for fracdiff
forecast(
object,
h = 10,
level = c(80, 95),
fan = FALSE,
lambda = object$lambda,
biasadj = NULL,
...
)
```# S3 method for Arima
forecast(
object,
h = ifelse(object$arma[5] > 1, 2 * object$arma[5], 10),
level = c(80, 95),
fan = FALSE,
xreg = NULL,
lambda = object$lambda,
bootstrap = FALSE,
npaths = 5000,
biasadj = NULL,
...
)

# S3 method for ar
forecast(
object,
h = 10,
level = c(80, 95),
fan = FALSE,
lambda = NULL,
bootstrap = FALSE,
npaths = 5000,
biasadj = FALSE,
...
)

object

An object of class "`Arima`

", "`ar`

" or
"`fracdiff`

". Usually the result of a call to
`arima`

, `auto.arima`

,
`ar`

, `arfima`

or
`fracdiff`

.

h

Number of periods for forecasting. If `xreg`

is used, `h`

is ignored and the number of forecast periods is set to the number of rows
of `xreg`

.

level

Confidence level for prediction intervals.

fan

If `TRUE`

, level is set to `seq(51,99,by=3)`

. This is
suitable for fan plots.

lambda

Box-Cox transformation parameter. If `lambda="auto"`

,
then a transformation is automatically selected using `BoxCox.lambda`

.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.

biasadj

Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.

...

Other arguments.

xreg

Future values of an regression variables (for class `Arima`

objects only). A numerical vector or matrix of external regressors; it should not be a data frame.

bootstrap

If `TRUE`

, then prediction intervals computed using
simulation with resampled errors.

npaths

Number of sample paths used in computing simulated prediction
intervals when `bootstrap=TRUE`

.

An object of class "`forecast`

".

The function `summary`

is used to obtain and print a summary of the
results, while the function `plot`

produces a plot of the forecasts and
prediction intervals.

The generic accessor functions `fitted.values`

and `residuals`

extract useful features of the value returned by `forecast.Arima`

.

An object of class "`forecast`

" is a list containing at least the
following elements:

A list containing information about the fitted model

The name of the forecasting method as a character string

Point forecasts as a time series

Lower limits for prediction intervals

Upper limits for prediction intervals

The confidence values associated with the prediction intervals

The original time series
(either `object`

itself or the time series used to create the model
stored as `object`

).

Residuals from the fitted model. That is x minus fitted values.

Fitted values (one-step forecasts)

For `Arima`

or `ar`

objects, the function calls
`predict.Arima`

or `predict.ar`

and
constructs an object of class "`forecast`

" from the results. For
`fracdiff`

objects, the calculations are all done within
`forecast.fracdiff`

using the equations given by Peiris and
Perera (1988).

Peiris, M. & Perera, B. (1988), On prediction with fractionally
differenced ARIMA models, *Journal of Time Series Analysis*,
**9**(3), 215-220.

`predict.Arima`

,
`predict.ar`

, `auto.arima`

,
`Arima`

, `arima`

, `ar`

,
`arfima`

.

# NOT RUN { fit <- Arima(WWWusage,c(3,1,0)) plot(forecast(fit)) library(fracdiff) x <- fracdiff.sim( 100, ma=-.4, d=.3)$series fit <- arfima(x) plot(forecast(fit,h=30)) # }