Returns a time series based on the model object object
.
# S3 method for ets
simulate(object, nsim = length(object$x), seed = NULL,
future = TRUE, bootstrap = FALSE, innov = NULL, ...)# S3 method for Arima
simulate(object, nsim = length(object$x), seed = NULL,
xreg = NULL, future = TRUE, bootstrap = FALSE, innov = NULL,
lambda = object$lambda, ...)
# S3 method for ar
simulate(object, nsim = object$n.used, seed = NULL,
future = TRUE, bootstrap = FALSE, innov = NULL, ...)
# S3 method for lagwalk
simulate(object, nsim = length(object$x),
seed = NULL, future = TRUE, bootstrap = FALSE, innov = NULL,
lambda = object$lambda, ...)
# S3 method for fracdiff
simulate(object, nsim = object$n, seed = NULL,
future = TRUE, bootstrap = FALSE, innov = NULL, ...)
# S3 method for nnetar
simulate(object, nsim = length(object$x), seed = NULL,
xreg = NULL, future = TRUE, bootstrap = FALSE, innov = NULL,
lambda = object$lambda, ...)
# S3 method for modelAR
simulate(object, nsim = length(object$x),
seed = NULL, xreg = NULL, future = TRUE, bootstrap = FALSE,
innov = NULL, lambda = object$lambda, ...)
An object of class "ets
", "Arima
", "ar
"
or "nnetar
".
Number of periods for the simulated series. Ignored if either
xreg
or innov
are not NULL
.
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.
Produce sample paths that are future to and conditional on the
data in object
. Otherwise simulate unconditionally.
Do simulation using resampled errors rather than normally
distributed errors or errors provided as innov
.
A vector of innovations to use as the error series. Ignored if
bootstrap==TRUE
. If not NULL
, the value of nsim
is set
to length of innov
.
Other arguments, not currently used.
New values of xreg
to be used for forecasting. The value
of nsim
is set to the number of rows of xreg
if it is not
NULL
.
Box-Cox transformation parameter. If lambda="auto"
,
then a transformation is automatically selected using BoxCox.lambda
.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.
An object of class "ts
".
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.
# NOT RUN {
fit <- ets(USAccDeaths)
plot(USAccDeaths, xlim=c(1973,1982))
lines(simulate(fit, 36), col="red")
# }
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