Make a new forecast from a Nested Modeltime Table.
Usage
modeltime_nested_forecast(
object,
h = NULL,
include_actual = TRUE,
conf_interval = 0.95,
conf_method = "conformal_default",
id_subset = NULL,
control = control_nested_forecast()
)
Arguments
- object
A Nested Modeltime Table
- h
The forecast horizon. Extends the "trained on" data "h" periods into the future.
- include_actual
Whether or not to include the ".actual_data" as part of the forecast. If FALSE, just returns the forecast predictions.
- 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
: Usesqnorm()
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)
- id_subset
A sequence of ID's from the modeltime table to subset the forecasting process. This can speed forecasts up.
- control
Used to control verbosity and parallel processing. See
control_nested_forecast()
.
Details
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:
extract_nested_future_forecast()
: Extracts the future forecast created after refitting withmodeltime_nested_refit()
.extract_nested_test_forecast()
: Extracts the test forecast created after initial fitting withmodeltime_nested_fit()
.
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 returnedIf
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.