To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains. However, they are difficult to apply to time series as the autocorrelative structure of time series violates basic assumptions required by conformal prediction. We propose HopCPT, a novel conformal prediction approach for time series that not only copes with temporal structures but leverages them. We show that our approach is theoretically well justified for time series where temporal dependencies are present. In experiments, we demonstrate that our new approach outperforms state-of-the-art conformal prediction methods on multiple real-world time series datasets from four different domains.

翻译：为量化不确定性，共形预测方法正获得越来越多的关注，并已成功应用于多个领域。然而，由于时间序列的自相关结构违反了共形预测所需的基本假设，这些方法难以直接应用于时间序列。我们提出HopCPT——一种新颖的共形预测方法，它不仅能够应对时间结构，更能加以利用。我们证明，对于存在时间依赖性的时间序列，该方法在理论上具有充分合理性。实验表明，我们的新方法在来自四个不同领域的多个真实世界时间序列数据集上，均优于现有的最先进共形预测方法。