# Fit Tidymodels Workflows to Nested Time Series

Source:`R/nested-modeltime_fit.R`

`modeltime_nested_fit.Rd`

Fits one or more `tidymodels`

workflow objects to nested time series data using the following process:

Models are iteratively fit to training splits.

Accuracy is calculated on testing splits and is logged. Accuracy results can be retrieved with

`extract_nested_test_accuracy()`

Any model that returns an error is logged. Error logs can be retrieved with

`extract_nested_error_report()`

Forecast is predicted on testing splits and is logged. Forecast results can be retrieved with

`extract_nested_test_forecast()`

## Usage

```
modeltime_nested_fit(
nested_data,
...,
model_list = NULL,
metric_set = default_forecast_accuracy_metric_set(),
conf_interval = 0.95,
conf_method = "conformal_default",
control = control_nested_fit()
)
```

## Arguments

- nested_data
Nested time series data

- ...
Tidymodels

`workflow`

objects that will be fit to the nested time series data.- model_list
Optionally, a

`list()`

of Tidymodels`workflow`

objects can be provided- metric_set
A

`yardstick::metric_set()`

that is used to summarize one or more forecast accuracy (regression) metrics.- 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)

- control
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:

Using

`...`

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

.