Make a new forecast from a Nested Modeltime Table.

  h = NULL,
  include_actual = TRUE,
  conf_interval = 0.95,
  id_subset = NULL,
  control = control_nested_forecast()



A Nested Modeltime Table


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


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


An estimated confidence interval based on the calibration data. This is designed to estimate future confidence from out-of-sample prediction error.


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


Used to control verbosity and parallel processing. See control_nested_forecast().


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:

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 returned

  • If 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.