In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation. In this context, we introduce the notion of parity calibration, which captures the goal of calibrated forecasting for the increase-decrease (or "parity") event in a timeseries. Parity probabilities can be extracted from a forecasted distribution for the output, but we show that such a strategy leads to theoretical unpredictability and poor practical performance. We then observe that although the original task was regression, parity calibration can be expressed as binary calibration. Drawing on this connection, we use an online binary calibration method to achieve parity calibration. We demonstrate the effectiveness of our approach on real-world case studies in epidemiology, weather forecasting, and model-based control in nuclear fusion.

翻译：在序列回归设定中，决策者可能主要关注未来观测值相对于当前观测值将上升还是下降，而非未来观测值的实际数值。在此背景下，我们提出"奇偶校准"这一概念，旨在针对时间序列中的升降（即"奇偶"）事件实现校准预测。虽然奇偶概率可以从输出的预测分布中提取，但我们证明这一策略会导致理论上的不可预测性及较差的实际表现。随后我们注意到，尽管原始任务属于回归问题，但奇偶校准可转化为二元校准。基于这一关联，我们运用在线二元校准方法实现奇偶校准。通过流行病学、天气预报以及核聚变中基于模型的控制等真实案例研究，我们验证了该方法的效果。