Fits one or more tidymodels workflow objects to nested time series data using the following process:

1. Models are iteratively fit to training splits.

2. Accuracy is calculated on testing splits and is logged. Accuracy results can be retrieved with extract_nested_test_accuracy()

3. Any model that returns an error is logged. Error logs can be retrieved with extract_nested_error_report()

4. Forecast is predicted on testing splits and is logged. Forecast results can be retrieved with extract_nested_test_forecast()

modeltime_nested_fit(
nested_data,
...,
model_list = NULL,
metric_set = default_forecast_accuracy_metric_set(),
conf_interval = 0.95,
control = control_nested_fit()
)

## Arguments

nested_data Nested time series data Tidymodels workflow objects that will be fit to the nested time series data. Optionally, a list() of Tidymodels workflow objects can be provided A yardstick::metric_set() that is used to summarize one or more forecast accuracy (regression) metrics. An estimated confidence interval based on the calibration data. This is designed to estimate future confidence from out-of-sample prediction error. Used to control verbosity and parallel processing. See control_nested_fit().

## Details

### Preparing Data for Nested Forecasting

Use extend_timeseries(), nest_timeseries(), and split_nested_timeseries() for preparing data for Nested Forecasting. The structure must be a nested data frame, which is suppplied in modeltime_nested_fit(nested_data).

### Fitting Models

Models must be in the form of tidymodels workflow objects. The models can be provided in two ways:

1. Using ... (dots): The workflow objects can be provided as dots.

2. Using model_list parameter: You can supply one or more workflow objects that are wrapped in a list().

### Controlling the fitting process

A control object can be provided during fitting to adjust the verbosity and parallel processing. See control_nested_fit().