forecast (version 7.1)

simulate.ets: Simulation from a time series model

Description

Returns a time series based on the model object object.

Usage

"simulate"(object, nsim=length(object$x), seed=NULL, future=TRUE,  bootstrap=FALSE, innov=NULL, ...)
"simulate"(object, nsim=object$n.used, seed=NULL, future=TRUE,  bootstrap=FALSE,  innov=NULL, ...)
"simulate"(object, nsim=length(object$x), seed=NULL, xreg=NULL, future=TRUE,  bootstrap=FALSE,  innov=NULL, lambda=object$lambda, ...)
"simulate"(object, nsim=object$n, seed=NULL, future=TRUE,  bootstrap=FALSE,  innov=NULL, ...)

Arguments

object

An object of class "ets", "Arima" or "ar".

nsim

Number of periods for the simulated series

seed

Either NULL or an integer that will be used in a call to set.seed before simulating the time series. The default, NULL will not change the random generator state.

future

Produce sample paths that are future to and conditional on the data in object.

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, bootstrap and seed are ignored.

xreg

New values of xreg to be used for forecasting. Must have nsim rows.

lambda

Box-Cox parameter. If not NULL, the simulated series is transformed using an inverse Box-Cox transformation with parameter lamda.

...

Other arguments.

Value

ts".

Details

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.

See Also

ets, Arima, auto.arima, ar, arfima.

Examples

plot(USAccDeaths,xlim=c(1973,1982))
lines(simulate(fit, 36),col="red")