# simulate.ets

From forecast v7.3
by Rob Hyndman

##### Simulation from a time series model

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

.

- Keywords
- ts

##### Usage

```
"simulate"(object, nsim=length(object$x), seed=NULL, future=TRUE, bootstrap=FALSE, innov=NULL, ...)
"simulate"(object, nsim=object$n.used, seed=NULL, future=TRUE, bootstrap=FALSE, innov=NULL, ...)
"simulate"(object, nsim=length(object$x), seed=NULL, xreg=NULL, future=TRUE, bootstrap=FALSE, innov=NULL, lambda=object$lambda, ...)
"simulate"(object, nsim=object$n, seed=NULL, future=TRUE, bootstrap=FALSE, innov=NULL, ...)
"simulate"(object, nsim=length(object$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
- 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`

. - bootstrap
- If TRUE, simulation uses 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`

. - xreg
- New values of xreg to be used for forecasting. Must have nsim rows.
- lambda
- Box-Cox parameter. If not
`NULL`

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

. - ...
- Other arguments.

##### 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

`ts`

".##### See Also

##### Examples

```
plot(USAccDeaths,xlim=c(1973,1982))
lines(simulate(fit, 36),col="red")
```

*Documentation reproduced from package forecast, version 7.3, License: GPL (>= 2)*

### Community examples

Looks like there are no examples yet.