提出新方法让预测不确定性始终可靠，无论数据如何突变。
Calibrated Probabilistic Forecasts for Arbitrary Sequences
- 用博弈论思想设计校准算法，确保任意数据下的预测可信
- 实测在能源系统中显著提升预测校准度与决策效果
- 可改造现有模型，不牺牲性能就能提升可靠性
真实世界的数据流可能因分布漂移、反馈环或对抗性行为而发生不可预测的变化，这会破坏预测的可靠性。本文提出一种预测框架，确保无论数据如何演变，其不确定性估计都保持有效。基于博弈论中的黑威尔可接近性概念，我们构建了适用于任意紧致空间（如分类或有界回归）的预测框架，能保证校准的不确定性。该框架还可用于重新校准已有预测器，在不损失预测性能的前提下实现校准。我们实现了通用梯度算法及针对常见特例优化的算法。实验表明，该方法在能源系统中显著提升了校准度和下游决策质量。
原文摘要 · Abstract (English)
Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors, which challenges the validity of forecasts. We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves. Leveraging the concept of Blackwell approachability from game theory, we introduce a forecasting framework that guarantees calibrated uncertainties for outcomes in any compact space (e.g., classification or bounded regression). We extend this framework to recalibrate existing forecasters, guaranteeing calibration without sacrificing predictive performance. We implement both general-purpose gradient-based algorithms and algorithms optimized for popular special cases of our framework. Empirically, our algorithms improve calibration and downstream decision-making for energy systems.
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