# simulate.ets

##### Simulation from a time series model

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

.

- Keywords
- ts

##### Usage

```
# 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 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, ...)

##### 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`

.- 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 parameter. If not

`NULL`

, the simulated series is transformed using an inverse Box-Cox transformation with parameter`lamda`

.

##### 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.

##### Value

An object of class "`ts`

".

##### See Also

##### Examples

```
# NOT RUN {
fit <- ets(USAccDeaths)
plot(USAccDeaths, xlim=c(1973,1982))
lines(simulate(fit, 36), col="red")
# }
```

*Documentation reproduced from package forecast, version 8.1, License: GPL (>= 3)*