Decomposition of the mean absolute error (MAE) into systematic and unsystematic components

When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River.

Automated summary

When evaluating the performance of quantitative models, measures like mean squared error (MSE) and root mean squared error (RMSE) are commonly used to characterize dimensioned errors. However, absolute-value-based measures like mean absolute error (MAE) are more interpretable for quantifying average error. This study develops and demonstrates a decomposition of MAE into three submeasures, providing more straightforward information on the distribution of model error and proving to be preferable to comparable decompositions of MSE.