Make a new forecast from a Nested Modeltime Table.

## Usage

```
modeltime_nested_forecast(
object,
h = NULL,
include_actual = TRUE,
conf_interval = 0.95,
conf_method = "conformal_default",
id_subset = NULL,
control = control_nested_forecast()
)
```

## Arguments

- object
A Nested Modeltime Table

- h
The forecast horizon. Extends the "trained on" data "h" periods into the future.

- include_actual
Whether or not to include the ".actual_data" as part of the forecast. If FALSE, just returns the forecast predictions.

- conf_interval
An estimated confidence interval based on the calibration data. This is designed to estimate future confidence from

*out-of-sample prediction error*.- conf_method
Algorithm used to produce confidence intervals. All CI's are Conformal Predictions. Choose one of:

`conformal_default`

: Uses`qnorm()`

to compute quantiles from out-of-sample (test set) residuals.`conformal_split`

: Uses the split method split conformal inference method described by Lei*et al*(2018)

- id_subset
A sequence of ID's from the modeltime table to subset the forecasting process. This can speed forecasts up.

- control
Used to control verbosity and parallel processing. See

`control_nested_forecast()`

.

## Details

This function is designed to help users that want to make new forecasts other than those that are created during the logging process as part of the Nested Modeltime Workflow.

### Logged Forecasts

The logged forecasts can be extracted using:

`extract_nested_future_forecast()`

: Extracts the future forecast created after refitting with`modeltime_nested_refit()`

.`extract_nested_test_forecast()`

: Extracts the test forecast created after initial fitting with`modeltime_nested_fit()`

.

The problem is that these forecasts are static. The user would need to redo the fitting, model selection,
and refitting process to obtain new forecasts. This is why `modeltime_nested_forecast()`

exists. So you can create
a new forecast without retraining any models.

### Nested Forecasts

The main arguments is
`h`

, which is a horizon that specifies how far into the future to make the new forecast.

If

`h = NULL`

, a logged forecast will be returnedIf

`h = 12`

, a new forecast will be generated that extends each series 12-periods into the future.If

`h = "2 years"`

, a new forecast will be generated that extends each series 2-years into the future.

Use the `id_subset`

to filter the Nested Modeltime Table `object`

to just the time series of interest.

Use the `conf_interval`

to override the logged confidence interval.
Note that this will have no effect if `h = NULL`

as logged forecasts are returned.
So be sure to provide `h`

if you want to update the confidence interval.

Use the `control`

argument to apply verbosity during the forecasting process and to run forecasts in parallel.
Generally, parallel is better if many forecasts are being generated.