Summarise the performance of the model using accuracy measures. Accuracy measures can be computed directly from models as the one-step-ahead fitted residuals are available. When evaluating accuracy on forecasts, you will need to provide a complete dataset that includes the future data and data used to train the model.
# S3 method for mdl_df
accuracy(object, measures = point_accuracy_measures, ...)# S3 method for mdl_ts
accuracy(object, measures = point_accuracy_measures, ...)
# S3 method for fbl_ts
accuracy(object, data, measures = point_accuracy_measures, ..., by = NULL)
A model or forecast object
A list of accuracy measure functions to compute (such as point_accuracy_measures, interval_accuracy_measures, or distribution_accuracy_measures)
Additional arguments to be passed to measures that use it.
A dataset containing the complete model dataset (both training and test data). The training portion of the data will be used in the computation of some accuracy measures, and the test data is used to compute the forecast errors.
Variables over which the accuracy is computed (useful for computing across forecast horizons in cross-validation). If by is NULL, groups will be chosen automatically from the key structure.