| simulate.ets {forecast} | R Documentation |
Returns a time series based on the model object object.
## S3 method for class 'ets'
simulate(object, nsim=length(object$x), seed=NULL, future=TRUE,
bootstrap=FALSE, innov=NULL, ...)
## S3 method for class 'ar'
simulate(object, nsim=object$n.used, seed=NULL, future=TRUE,
bootstrap=FALSE, innov=NULL, ...)
## S3 method for class 'Arima'
simulate(object, nsim=length(object$x), seed=NULL, xreg=NULL, future=TRUE,
bootstrap=FALSE, innov=NULL, lambda=object$lambda, ...)
## S3 method for class 'fracdiff'
simulate(object, nsim=object$n, seed=NULL, future=TRUE,
bootstrap=FALSE, innov=NULL, ...)
object |
An object of class " |
nsim |
Number of periods for the simulated series |
seed |
Either NULL or an integer that will be used in a call to |
future |
Produce sample paths that are future to and conditional on the data in |
bootstrap |
If TRUE, simulation uses resampled errors rather than normally distributed errors. |
innov |
A vector of innovations to use as the error series. If present, |
xreg |
New values of xreg to be used for forecasting. Must have nsim rows. |
lambda |
Box-Cox parameter. If not |
... |
Other arguments. |
With simulate.Arima(), the object should be produced by Arima or auto.arima, rather than arima. By default, the error series is assumed normally distributed and generated using rnorm. If innov is present, it is used instead. If bootstrap=TRUE and innov=NULL, the residuals are resampled instead.
When future=TRUE, the sample paths are conditional on the data. When future=FALSE and the model is stationary, the sample paths do not depend on the data at all. When future=FALSE and the model is non-stationary, the location of the sample paths is arbitrary, so they all start at the value of the first observation.
An object of class "ts".
Rob J Hyndman
ets, Arima, auto.arima, ar, arfima.
fit <- ets(USAccDeaths) plot(USAccDeaths,xlim=c(1973,1982)) lines(simulate(fit, 36),col="red")