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,
...
)

object

An object of class "`ets`

", "`Arima`

", "`ar`

"
or "`nnetar`

".

nsim

Number of periods for the simulated series. Ignored if either
`xreg`

or `innov`

are not `NULL`

.

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`

. Otherwise simulate unconditionally.

bootstrap

Do simulation using resampled errors rather than normally
distributed errors or errors provided as `innov`

.

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.

xreg

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`

.

lambda

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") # }