为图上时序数据提供可信赖的预测置信区域，兼顾图结构与时间动态。
Conformal Inference for Time Series over Graphs
- 利用图结构捕捉节点间依赖，构建时序图数据的预测区间
- 预测区域体积比传统方法减少最多80%，覆盖率达预期水平
- 适合需要高精度不确定性量化的真实网络动态系统
在联网动态环境中实现可信决策，亟需针对图时序数据的新型不确定性量化方法。现有合规预测（CP）方法分别应用于多变量时间序列和静态图，但或忽略图拓扑，或忽视时间动态。为此，我们提出一种面向图时序数据的基于CP的序列预测区域框架。关键技术在于利用图结构捕获节点间的成对依赖关系，同时保证预测结果的用户指定覆盖率。理论证明，该方法使椭球预测集体积相对于无图依赖的方法呈指数级缩小。在真实数据集上验证表明，新框架在保持理想经验覆盖率的同时，预测区域显著缩小（最多减少80%），优于现有方法。
原文摘要 · Abstract (English)
Trustworthy decision making in networked, dynamic environments calls for innovative uncertainty quantification substrates in predictive models for graph time series. Existing conformal prediction (CP) methods have been applied separately to multivariate time series and static graphs, but they either ignore the underlying graph topology or neglect temporal dynamics. To bridge this gap, here we develop a CP-based sequential prediction region framework tailored for graph time series. A key technical innovation is to leverage the graph structure and thus capture pairwise dependencies across nodes, while providing user-specified coverage guarantees on the predictive outcomes. We formally establish that our scheme yields an exponential shrinkage in the volume of the ellipsoidal prediction set relative to its graph-agnostic counterpart. Using real-world datasets, we demonstrate that the novel uncertainty quantification framework maintains desired empirical coverage while achieving markedly smaller (up to 80% reduction) prediction regions than existing approaches.
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