Mean Percentage Error

Measure of the average signed percentage error of forecasts. Positive values indicate systematic under-forecasting, negative values indicate over-forecasting.

Details

$$ \mathrm{MPE} = \frac{100}{n} \sum_{i=1}^n \frac{y_i - \hat y_i}{y_i} $$

Dictionary

This mlr3::Measure can be instantiated via the dictionary mlr3::mlr_measures or with the associated sugar function mlr3::msr():

mlr_measures$get("fcst.mpe")
msr("fcst.mpe")

Meta Information

• Task type: “regr”

• Range: \((-\infty, \infty)\)

• Minimize: NA

• Average: macro

• Required Prediction: “response”

• Required Packages: mlr3, mlr3forecast

Parameters

Empty ParamSet

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.

• Dictionary of Measures: mlr3::mlr_measures

• 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.rmsse, mlr_measures_fcst.winkler

Super classes

mlr3::Measure -> mlr3::MeasureRegr -> MeasureMPE

Methods

Public methods

• MeasureMPE$new()

• MeasureMPE$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$obs_loss()
• mlr3::Measure$print()
• mlr3::Measure$score()

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Method new()

Creates a new instance of this R6 class.

Usage

MeasureMPE$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureMPE$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.