Measure of the total absolute error of forecasts as a percentage of the total absolute truth. It weights each error by the magnitude of the series, making it robust to individual observations close to zero where the ordinary percentage error is undefined.
Details
$$ \mathrm{WAPE} = 100 \cdot \frac{\sum_{i=1}^n \lvert y_i - \hat y_i \rvert}{\sum_{i=1}^n \lvert y_i \rvert} $$
Dictionary
This mlr3::Measure can be instantiated via the dictionary mlr3::mlr_measures or with the associated sugar function mlr3::msr():
Meta Information
Task type: “regr”
Range: \([0, \infty)\)
Minimize: TRUE
Average: macro
Required Prediction: “response”
Required Packages: mlr3, mlr3forecast
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval
Package mlr3measures for the scoring functions.
as.data.table(mlr_measures)for a table of available Measures in the running session (depending on the loaded packages).Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
mlr_measures_fcst.acf1,
mlr_measures_fcst.coverage,
mlr_measures_fcst.mase,
mlr_measures_fcst.mda,
mlr_measures_fcst.mdpv,
mlr_measures_fcst.mdv,
mlr_measures_fcst.mpe,
mlr_measures_fcst.msis,
mlr_measures_fcst.pinball,
mlr_measures_fcst.rmsse,
mlr_measures_fcst.winkler
Super classes
mlr3::Measure -> mlr3::MeasureRegr -> MeasureWAPE