Produces forecasts from a trained model.

```
# S3 method for NNETAR
forecast(
object,
new_data,
specials = NULL,
simulate = TRUE,
bootstrap = FALSE,
times = 5000,
...
)
```

## Arguments

- object
A model for which forecasts are required.

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

- specials
(passed by `fabletools::forecast.mdl_df()`

).

- simulate
If `TRUE`

, forecast distributions are produced by sampling from a normal distribution. Without simulation, forecast uncertainty cannot be estimated for this model and instead a degenerate distribution with the forecast mean will be produced.

- bootstrap
If `TRUE`

, forecast distributions are produced by sampling from the model's training residuals.

- times
The number of sample paths to use in producing the forecast distribution. Setting `simulate = FALSE`

or `times = 0`

will produce degenerate forecast distributions of the forecast mean.

- ...
Other arguments passed to methods

## Value

A list of forecasts.

## Examples

```
as_tsibble(airmiles) %>%
model(nn = NNETAR(box_cox(value, 0.15))) %>%
forecast(times = 10)
#> # A fable: 2 x 4 [1Y]
#> # Key: .model [1]
#> .model index value .mean
#> <chr> <dbl> <dist> <dbl>
#> 1 nn 1961 sample[10] 31085.
#> 2 nn 1962 sample[10] 33936.
```