ITSY：从不规则时间序列数据中进行因果发现

中文摘要 AI 辅助

结构因果模型用于时间序列可以恢复同期和滞后效应，但大多数方法需要完整的观测窗口，当样本缺失时会出现设定错误。我们提出了ITSY，这是在线性模型下从不规则时间序列中进行因果发现的第一种连续优化方法。ITSY重新构造了结构方程，使得预测使用最近可用的历史而不是可能缺失的当前切片，并在学习两个图的同时联合插补缺失值。加权重建目标纠正了由这种重新构造引起的噪声变换。在改变缺失率、尺度、图密度和噪声的多种合成设置中，以及在一个真实世界基准上，ITSY相对于代表性的基于SCM的基线持续提高了图恢复性能，证明了所提出方法的有效性。结果为不规则线性一阶动力学提供了一个聚焦的解决方案，并阐明了非线性或高阶扩展所需的假设。

英文摘要

Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become misspecified when samples are missing. We introduce ITSY, the first continuous-optimization method for causal discovery from irregular time series under a linear model. ITSY reformulates the structural equation so that prediction uses the nearest available history rather than the possibly missing current slice, and jointly imputes missing values while learning both graphs. A weighted reconstruction objective corrects the noise transformation induced by this reformulation. Across synthetic regimes varying missingness, scale, graph density, and noise, and on a real world benchmark, ITSY consistently improves graph recovery over representative SCM-based baselines, demonstrating the effectiveness of the proposed method. The results establish a focused solution for irregular linear first-order dynamics and clarify the assumptions required for nonlinear or higher-order extensions.

发表机构

• Renmin University of China（中国人民大学）
• Zhejiang University（浙江大学）

机构由 AI 辅助整理，请以论文原文为准。