Creating different forecast paths for forecast objects (when applicable),
by utilizing the underlying model distribution with the `simulate`

function.

## Usage

```
ts_forecast_simulator(
.model,
.data,
.ext_reg = NULL,
.frequency = NULL,
.bootstrap = TRUE,
.horizon = 4,
.iterations = 25,
.sim_color = "steelblue",
.alpha = 0.05
)
```

## Arguments

- .model
A forecasting model of one of the following from the

`forecast`

package:`Arima()`

with xreg

- .data
The data that is used for the

`.model`

parameter. This is used with`timetk::tk_index()`

- .ext_reg
A

`tibble`

or`matrix`

of future xregs that should be the same length as the horizon you want to forecast.- .frequency
This is for the conversion of an internal table and should match the time frequency of the data.

- .bootstrap
A boolean value of TRUE/FALSE. From

`forecast::simulate.Arima()`

Do simulation using resampled errors rather than normally distributed errors.- .horizon
An integer defining the forecast horizon.

- .iterations
An integer, set the number of iterations of the simulation.

- .sim_color
Set the color of the simulation paths lines.

- .alpha
Set the opacity level of the simulation path lines.

## Details

This function expects to take in a model of either `Arima`

,
`auto.arima`

, `ets`

or `nnetar`

from the `forecast`

package. You can supply a
forecasting horizon, iterations and a few other items. You may also specify
an Arima() model using xregs.

## See also

Other Simulator:
`ts_arima_simulator()`

## Examples

```
suppressPackageStartupMessages(library(forecast))
suppressPackageStartupMessages(library(dplyr))
# Create a model
fit <- auto.arima(AirPassengers)
data_tbl <- ts_to_tbl(AirPassengers)
# Simulate 50 possible forecast paths, with .horizon of 12 months
output <- ts_forecast_simulator(
.model = fit
, .horizon = 12
, .iterations = 50
, .data = data_tbl
)
output$ggplot
```