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")