object
."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, ...)
ets
", "Arima
" or "ar
".set.seed
before simulating the time series. The default, NULL will not change the random generator state.object
.bootstrap
and seed
are ignored.NULL
, the simulated series is transformed using an inverse Box-Cox transformation with parameter lamda
.ts
".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.
ets
, Arima
, auto.arima
, ar
, arfima
.plot(USAccDeaths,xlim=c(1973,1982))
lines(simulate(fit, 36),col="red")
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