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，保形预测方法正获得越来越广泛的关注，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保形预测所需的基本假设，因此难以将其应用于时间序列。我们提出HopCPT，一种新颖的时间序列保形预测方法，该方法不仅能够应对时间结构，还能充分利用这些结构。我们证明，在存在时间依赖性的时间序列中，我们的方法具有理论上的充分合理性。在实验中，我们展示了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优于现有的最佳保形预测方法。