modeltime_nested_fit {modeltime} | R Documentation |
Fit Tidymodels Workflows to Nested Time Series
Description
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 |
model_list |
Optionally, a |
metric_set |
A |
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:
|
control |
Used to control verbosity and parallel processing. See |
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 alist()
.
Controlling the fitting process
A control
object can be provided during fitting to adjust the verbosity and parallel processing.
See control_nested_fit()
.