Local scale invariance and robustness of proper scoring rules

Averages of proper scoring rules are often used to rank proba-bilistic forecasts. In many cases, the individual terms in these averages are based on observations and forecasts from different distributions. We show that some of the most popular proper scoring rules, such as the continuous ranked probability score (CRPS), give more importance to observations with large uncertainty, which can lead to unintuitive rankings. To describe this is-sue, we define the concept of local scale invariance for scoring rules. A new class of generalized proper kernel scoring rules is derived and as a member of this class we propose the scaled CRPS (SCRPS). This new proper scor-ing rule is locally scale invariant and, therefore, works in the case of varying uncertainty. Like the CRPS, it is computationally available for output from ensemble forecasts, and does not require the ability to evaluate densities of forecasts. We further define robustness of scoring rules, show why this also can be an important concept for average scores unless one is specifically interested in extremes, and derive new proper scoring rules that are robust against outliers. The theoretical findings are illustrated in three different applications from spatial statistics, stochastic volatility models and regression for count data.

Automated summary

Averages of proper scoring rules are commonly used to rank probabilistic forecasts. However, some popular scoring rules give more importance to observations with large uncertainty, resulting in unintuitive rankings. To address this issue, we propose a new proper scoring rule called scaled CRPS (SCRPS), which is locally scale invariant and works in varying uncertainty situations.