# simulate.ets

From forecast v5.3
by Rob Hyndman

##### Simulation from a time series model

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

.

- Keywords
- ts

##### Usage

```
## S3 method for class 'ets':
simulate(object, nsim=length(object$x), seed=NULL, future=TRUE,
bootstrap=FALSE, innov=NULL, ...)
## S3 method for class 'ar':
simulate(object, nsim=object$n.used, 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 'fracdiff':
simulate(object, nsim=object$n, seed=NULL, future=TRUE,
bootstrap=FALSE, innov=NULL, ...)
```

##### Arguments

- object
- An object of class "
`ets`

", "`Arima`

" or "`ar`

". - 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.
- innov
- A vector of innovations to use as the error series. If present,
`bootstrap`

and`seed`

are ignored. - 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.

##### Value

- An object of class "
`ts`

".

##### See Also

`ets`

, `Arima`

, `auto.arima`

, `ar`

, `arfima`

.

##### Examples

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

*Documentation reproduced from package forecast, version 5.3, License: GPL (>= 2)*

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