modeltime_nested_fit function

Fit Tidymodels Workflows to Nested Time Series

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,
  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:

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().