forecast (version 2.05)

forecast.Arima: 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,...)
## S3 method for class 'ar':
forecast(object, h=10, level=c(80,95), fan=FALSE, ...)
## S3 method for class 'fracdiff':
forecast(object, h=10, level=c(80,95), fan=FALSE, ...)

Arguments

object

An object of class "Arima", "ar" or "fracdiff". Usually the result of a call to arima, auto.arima,

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).

...

Other arguments.

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)

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).

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

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))