发布时间: 2018-03-29 浏览次数: 10

报告题目:Forecast Interpretation and Evaluation





杜海良,统计学博士,2009年博士毕业敦政治经济学院。毕业以后至2013年在敦政治经济学院时间序列研究中心和气候经济政策研究中心担任研究从事时间序列分析以及非线力学理研究。2014年加入芝加哥大学气候能源决策研究中心从事气候模型可靠性分析。于2017加入杜大学数学科学系从事能源系不确定性量化分析。2018 被杜大学任命助理教授。


The evaluation of forecast performance plays a central role both in the interpretation and in the use of forecast systems and their development. Many forecast systems are available, but evaluations of their performance are not standardized, with many different scores being used to measure different aspects of performance. Ensemble interpretations which interpret a probability forecast as a point forecast (a delta function such as the ensemble mean) or as a collection of delta functions (reflecting, for example, the position of each ensemble member) may provide misleading estimates of skill in nonlinear systems as they fail to consider all the probabilistic information available. Even when the discussion is restricted to proper scores, there remains considerable variability between scores in terms of their sensitivity to outcomes in regions of low (or vanishing) probability; proper scores need not rank competing forecast systems in the same order when each forecast system is imperfect. The locality property is explored to further distinguish skill scores. The only local proper score, the logarithmic score, has an immediate interpretation in terms of bits of information. Nonlocal proper scores considered are shown to have properties that could produce counter intuitive evaluations. It is suggested that the logarithmic score always be included in the evaluation of probabilistic forecasts.