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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:

  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()

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:

  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().