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 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` . |

## Value

An object of class "`ts`

".

## 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.

## See also

`ets`

, `Arima`

, `auto.arima`

,
`ar`

, `arfima`

, `nnetar`

.

## Examples

lines(simulate(fit, 36), col="red")