simulate.ets | R Documentation |
Returns a time series based on the model object object
.
## S3 method for class 'ets'
simulate(
object,
nsim = length(object$x),
seed = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
...
)
## S3 method for class 'Arima'
simulate(
object,
nsim = length(object$x),
seed = NULL,
xreg = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
lambda = object$lambda,
...
)
## S3 method for class 'ar'
simulate(
object,
nsim = object$n.used,
seed = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
...
)
## S3 method for class 'lagwalk'
simulate(
object,
nsim = length(object$x),
seed = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
lambda = object$lambda,
...
)
## S3 method for class 'fracdiff'
simulate(
object,
nsim = object$n,
seed = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
...
)
## S3 method for class 'nnetar'
simulate(
object,
nsim = length(object$x),
seed = NULL,
xreg = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
lambda = object$lambda,
...
)
## S3 method for class 'modelAR'
simulate(
object,
nsim = length(object$x),
seed = NULL,
xreg = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
lambda = object$lambda,
...
)
## S3 method for class 'tbats'
simulate(
object,
nsim = length(object$y),
seed = NULL,
future = TRUE,
bootstrap = FALSE,
innov = NULL,
...
)
object |
An object of class " |
nsim |
Number of periods for the simulated series. Ignored if either
|
seed |
Either |
future |
Produce sample paths that are future to and conditional on the
data in |
bootstrap |
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
|
... |
Other arguments, not currently used. |
xreg |
New values of |
lambda |
Box-Cox transformation parameter. If |
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
, nnetar
.
fit <- ets(USAccDeaths)
plot(USAccDeaths, xlim = c(1973, 1982))
lines(simulate(fit, 36), col = "red")
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