## 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$lambda,
bootstrap=FALSE, npaths=5000, ...)
## S3 method for class 'ar':
forecast(object, h=10, level=c(80,95), fan=FALSE, lambda=NULL,
bootstrap=FALSE, npaths=5000, ...)
## S3 method for class 'fracdiff':
forecast(object, h=10, level=c(80,95), fan=FALSE, lambda=object$lambda, ...)
Arima
", "ar
" or "fracdiff
". Usually the result of a call to
arima
, auto.arima
,
xreg
is used, h
is ignored and the number of forecast periods is
set to the number of rows of xreg
.TRUE
, level is set to seq(51,99,by=3)
. This is suitable for fan plots.Arima
objects only).TRUE
, then prediction intervals computed using simulation with resampled errors.bootstrap=TRUE
.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:
object
itself or the time series used to create the model stored as object
).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).predict.Arima
, predict.ar
, auto.arima
, Arima
,
arima
, ar
, arfima
.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))
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