Measures the mean absolute error of the forecast scaled by the in-sample mean absolute error of the naive (or seasonal naive) forecast. Values less than one indicate the forecast is better than the naive baseline.
Details
$$ \mathrm{MASE} = \frac{1}{n} \sum_{i=1}^n \frac{\lvert y_i - \hat y_i \rvert} {\frac{1}{T-m} \sum_{t=m+1}^T \lvert z_t - z_{t-m} \rvert} $$ where \(z\) is the training series, \(m\) is the seasonal period, and \(T\) is the length of the training series.
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
References
Hyndman, J R, Koehler, B A (2006). “Another look at measures of forecast accuracy.” International Journal of Forecasting, 22(4), 679–688.
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.mda,
mlr_measures_fcst.mdpv,
mlr_measures_fcst.mdv,
mlr_measures_fcst.mpe,
mlr_measures_fcst.rmsse,
mlr_measures_fcst.winkler
Super classes
mlr3::Measure -> mlr3::MeasureRegr -> MeasureMASE