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Name: Thibault Modeste

Talk Title: Scoring and validation of dynamic probability forecast

Abstract: Forecast and its evaluation are major task in statistic. In real applications, forecast often take the form of a dynamic process evolving over time and this sequential point of view must be taken into account. We propose and discuss a minimal framework for dynamic probability forecast and its evaluation. Proper scoring rules are a crucial concept for probability forecast evaluation and we show, under minimal assumptions, that they can still be used  in the dynamic framework because they are minimized, in the sense of the long term average score, by the ideal forecast. Another strategy for forecast evaluation is calibration theory based on the probability integral transform. Here ideal forecast is characterized by conditional calibration and we present some new tests based on regression trees that we compare to the ones proposed by Straehl and Ziegel (EJS 2017) in the framework of cross-calibration.

This talk is a contributed talk at EVA 2021.