Simulates future paths from a dataset using a fitted model. Innovations are
sampled by the model's assumed error distribution. If `bootstrap`

is `TRUE`

,
innovations will be sampled from the model's residuals. If `new_data`

contains the `.innov`

column, those values will be treated as innovations.

```
# S3 method for class 'ETS'
generate(x, new_data, specials, bootstrap = FALSE, ...)
```

- x
A fitted model.

- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.

- specials
(passed by

`fabletools::forecast.mdl_df()`

).- bootstrap
If

`TRUE`

, then forecast distributions are computed using simulation with resampled errors.- ...
Other arguments passed to methods

```
as_tsibble(USAccDeaths) %>%
model(ETS(log(value) ~ season("A"))) %>%
generate(times = 100)
#> # A tsibble: 2,400 x 4 [1M]
#> # Key: .model, .rep [100]
#> .model index .rep .sim
#> <chr> <mth> <chr> <dbl>
#> 1 "ETS(log(value) ~ season(\"A\"))" 1979 Jan 1 8244.
#> 2 "ETS(log(value) ~ season(\"A\"))" 1979 Feb 1 7226.
#> 3 "ETS(log(value) ~ season(\"A\"))" 1979 Mar 1 7913.
#> 4 "ETS(log(value) ~ season(\"A\"))" 1979 Apr 1 8342.
#> 5 "ETS(log(value) ~ season(\"A\"))" 1979 May 1 9437.
#> 6 "ETS(log(value) ~ season(\"A\"))" 1979 Jun 1 9440.
#> 7 "ETS(log(value) ~ season(\"A\"))" 1979 Jul 1 10092.
#> 8 "ETS(log(value) ~ season(\"A\"))" 1979 Aug 1 9126.
#> 9 "ETS(log(value) ~ season(\"A\"))" 1979 Sep 1 8183.
#> 10 "ETS(log(value) ~ season(\"A\"))" 1979 Oct 1 8750.
#> # ℹ 2,390 more rows
```