R: Forecasting using ARIMA or ARFIMA models

forecast.Arima {forecast}

R Documentation

Forecasting using ARIMA or ARFIMA models

Description

Returns forecasts and other information for univariate ARIMA models.

Usage

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

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(50,99,by=1)`. This is suitable for fan plots.
`xreg`	Future values of an regression variables (for class `Arima` objects only).
`lambda`	Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
`...`	Other arguments.

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:

`model`	A list containing information about the fitted model
`method`	The name of the forecasting method as a character string
`mean`	Point forecasts as a time series
`lower`	Lower limits for prediction intervals
`upper`	Upper limits for prediction intervals
`level`	The confidence values associated with the prediction intervals
`x`	The original time series (either `object` itself or the time series used to create the model stored as `object`).
`residuals`	Residuals from the fitted model. That is x minus fitted values.
`fitted`	Fitted values (one-step forecasts)

Author(s)

Rob J Hyndman

References

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

Examples

fit <- Arima(WWWusage,c(3,1,0))
plot(forecast(fit))

x <- fracdiff.sim( 100, ma=-.4, d=.3)$series
fit <- arfima(x)
plot(forecast(fit,h=30))

[Package forecast version 3.24 Index]