论文标题
时间序列设置的共形预测
Conformal prediction set for time-series
论文作者
论文摘要
当构建回归的预测间隔（具有实值响应）或分类的预测集（具有分类响应）时，不确定性量化对于研究复杂的机器学习方法至关重要。在本文中，我们基于[Xu and Xie，2021]的先前工作，开发了集合正规化的自适应预测集（ERAP），以构建时间序列（具有分类响应）的预测集（具有分类响应）。特别是，我们允许未知的依赖性存在于顺序到达的功能和响应中。在方法论方面，ERAPS是一种适用于任意分类器的基于无分配和集合的框架。从理论上讲，我们在不假设数据交换性的情况下绑定了覆盖差距并显示渐近集收敛。从经验上讲，我们通过ERAP展示了有效的边际和条件覆盖范围，而与竞争方法相比，这也倾向于产生更小的预测集。
When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we develop Ensemble Regularized Adaptive Prediction Set (ERAPS) to construct prediction sets for time-series (with categorical responses), based on the prior work of [Xu and Xie, 2021]. In particular, we allow unknown dependencies to exist within features and responses that arrive in sequence. Method-wise, ERAPS is a distribution-free and ensemble-based framework that is applicable for arbitrary classifiers. Theoretically, we bound the coverage gap without assuming data exchangeability and show asymptotic set convergence. Empirically, we demonstrate valid marginal and conditional coverage by ERAPS, which also tends to yield smaller prediction sets than competing methods.
