Returns a time series based on the model object `object`

.

## Usage

```
# 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,
...
)
```

## Arguments

- 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`

. Otherwise the default is the length of series used to train model (or 100 if no data found).- 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.

## Details

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.