发表机构
University of Leipzig; Karlsruhe Institute of Technology（莱比锡大学; 卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出结果条件重校准方法，在用户定义的结果区域上校准概率预测，改善极端事件等特定结果的校准，并在回归基准和电价预测中验证其有效性。
AI 中文摘要
校准是概率预测用于决策制定的基本要求。虽然最先进的预测方法往往会产生未校准的预测分布，但已有多种事后重校准方案被提出以生成校准预测。然而，流行的重校准方案可能会掩盖结果空间中特定区域的未校准问题。由于特定结果（如极端事件）通常对决策最为重要，当评估局限于这些结果时，概率预测应当被校准。因此，在本文中，我们引入了结果条件重校准，一种在用户定义的结果空间区域上对概率预测进行事后重校准的方法。该方法简单、易于实现，并可应用于任意预测分布。其工作原理是将Kuleshov等人（2018）的分位数重校准方法应用于预测条件分布，然后重新缩放这些条件分布，使得预测事件概率与经验发生频率相匹配。这产生了有效且连续的预测分布，并在每个感兴趣区域内得到校准。在回归基准测试中，我们证明了当关注特定结果时，现有的重校准方案不一定能产生校准预测，而我们的方法相对于现有的条件和非条件重校准方法，在结果条件校准方面有所改进，同时保持了整体校准的竞争力。在日前电价预测的应用中，该方法在预测负价格时显著改善了校准，而对预测准确性的影响可忽略不计。
英文摘要
Calibration is an essential requirement for probabilistic predictions to be useful for decision making. While state-of-the-art prediction methods often yield miscalibrated predictive distributions, several post-hoc recalibration schemes have been proposed to generate calibrated predictions. However, popular recalibration schemes can conceal miscalibration in specific regions of the outcome space. Since particular outcomes, such as extreme events, often matter most for decision making, probabilistic predictions should be calibrated when evaluation is restricted to these outcomes. Hence, in this paper, we introduce outcome-conditional recalibration, a post-hoc method to recalibrate probabilistic predictions on user-defined regions of the outcome space. The method is simple, easy to implement, and can be applied to arbitrary predictive distributions. It works by applying the quantile recalibration approach of Kuleshov et al. (2018) to forecast conditional distributions, before rescaling these conditional distributions so that forecast event probabilities match empirical occurrence frequencies. This produces valid and continuous predictive distributions that are calibrated within each region of interest. Across regression benchmarks, we demonstrate that existing recalibration schemes do not necessarily yield calibrated predictions when interest is on particular outcomes, and that our approach improves outcome-conditional calibration relative to existing conditional and unconditional recalibration methods, while retaining competitive calibration overall. In an application to day-ahead electricity price forecasting, the approach substantially improves calibration when predicting negative prices, at negligible cost to forecast accuracy.
