# forecast.fracdiff

##### Forecasting using ARIMA or ARFIMA models

Returns forecasts and other information for univariate ARIMA models.

- Keywords
- ts

##### Usage

```
# 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, ...)

##### Arguments

- 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. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.

- biasadj
Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities. By default, the value is taken from what was used when fitting the model.

- ...
Other arguments.

- xreg
Future values of an regression variables (for class

`Arima`

objects only).- 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`

.

##### Details

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).

##### Value

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)

##### References

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

##### See Also

`predict.Arima`

,
`predict.ar`

, `auto.arima`

,
`Arima`

, `arima`

, `ar`

,
`arfima`

.

##### Examples

```
# 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))
# }
```

*Documentation reproduced from package forecast, version 8.2, License: GPL-3*