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 accurate uncertainties 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.

翻译：现实世界的数据流可能因分布漂移、反馈循环和对抗行为而发生不可预测的变化，这对预测的有效性提出了挑战。我们提出了一个预测框架，无论数据如何演化都能确保有效的不确定性估计。利用博弈论中的布莱克韦尔可逼近性概念，我们引入了一个预测框架，该框架保证对任意紧致空间（例如分类或有界回归）中的结果提供校准的不确定性估计。我们扩展此框架以重新校准现有预测器，在保证不确定性准确性的同时不牺牲预测性能。我们实现了通用的基于梯度的算法，以及针对我们框架中常见特殊情况进行优化的算法。实验表明，我们的算法在能源系统中显著改善了校准效果和下游决策质量。