"Calibeating": Beating Forecasters at Their Own Game
This work addresses a fundamental issue in forecasting evaluation for researchers and practitioners, offering incremental improvements to existing methods.
The paper tackles the problem of improving forecasters' calibration without sacrificing their expertise, a concept termed 'calibeating', by providing deterministic and stochastic online procedures that achieve this, including extensions to multiple procedures and continuous calibration.
In order to identify expertise, forecasters should not be tested by their calibration score, which can always be made arbitrarily small, but rather by their Brier score. The Brier score is the sum of the calibration score and the refinement score; the latter measures how good the sorting into bins with the same forecast is, and thus attests to "expertise." This raises the question of whether one can gain calibration without losing expertise, which we refer to as "calibeating." We provide an easy way to calibeat any forecast, by a deterministic online procedure. We moreover show that calibeating can be achieved by a stochastic procedure that is itself calibrated, and then extend the results to simultaneously calibeating multiple procedures, and to deterministic procedures that are continuously calibrated.