提出因果分析方法，精准定位有延迟依赖的系统异常根源。
Causal explanations of outliers in systems with lagged time-dependencies
- 基于严格因果定义，处理时间依赖系统的无限依赖图
- 通过截断策略实现异常根源在特征与时间域的精确定位
- 适用于能源管理等具延迟效应的工业系统
在具有时间依赖性的受控系统中进行根因分析是一项重大挑战，尤其在能源系统中更为复杂，因其同时存在瞬时与延迟效应，若配备储能则具有记忆特性。本文将 Budhathoki 等人 [2022] 提出的因果根因分析方法扩展至一般时变系统，因其可视为“根因”的严格因果定义。特别地，讨论了两种截断策略以应对时变系统中存在的无限依赖图：一种保持因果机制完整，另一种对起始节点的机制进行近似。通过一个受工厂能源管理问题启发的高难度数据生成过程进行基准测试，结果表明，在足够多滞后步数下，该方法能够有效定位特征空间和时间维度上的根因。同时分析了机制近似的影响。
原文摘要 · Abstract (English)
Root-cause analysis in controlled time dependent systems poses a major challenge in applications. Especially energy systems are difficult to handle as they exhibit instantaneous as well as delayed effects and if equipped with storage, do have a memory. In this paper we adapt the causal root-cause analysis method of Budhathoki et al. [2022] to general time-dependent systems, as it can be regarded as a strictly causal definition of the term "root-cause". Particularly, we discuss two truncation approaches to handle the infinite dependency graphs present in time-dependent systems. While one leaves the causal mechanisms intact, the other approximates the mechanisms at the start nodes. The effectiveness of the different approaches is benchmarked using a challenging data generation process inspired by a problem in factory energy management: the avoidance of peaks in the power consumption. We show that given enough lags our extension is able to localize the root-causes in the feature and time domain. Further the effect of mechanism approximation is discussed.
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