基于线性时间序列数据的差异图发现进行根本原因分析
Root cause analysis via difference graph discovery from linear time-series data
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中文总结 AI 辅助
该研究针对线性时间序列的根本原因分析问题，将差异图发现方法适配到时间序列场景，经模拟数据与IT、重症监护真实数据集验证，可定位导致异常行为的因果机制。
中文摘要 AI 辅助
根本原因分析旨在识别复杂动力系统中导致异常的机制。本文从差异图发现的角度研究线性时间序列的根本原因分析，聚焦于抵抗效应的根本原因，对应于在正常状态与异常状态之间因果系数发生变化的变量。我们采用线性离散时间动态结构因果模型对该问题进行形式化，并将最初为发现两个总体间差异图而提出的多种方法适配到时间序列场景中，其中两个总体被替换为正常状态与异常状态。我们先在模拟数据上评估所提方法，再在来自IT监控和重症监护监控的真实世界数据集上验证其实用性。结果表明，差异图发现可帮助定位导致异常行为的因果机制。
英文摘要
Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis in linear time-series through the lens of difference graph discovery. We focus on effect-defying root causes, corresponding to variables whose causal coefficients change between a normal and an anomalous regime. We formalize this problem using linear discrete-time dynamic structural causal models and adapt several methods originally introduced for discovering difference graphs between two populations to the time-series setting, where the two populations are replaced by a normal and an anomalous regime. We first evaluate the proposed approaches on simulated data, and then demonstrate their practical relevance on real-world datasets from IT monitoring and intensive care monitoring. Our results show how difference graph discovery can help localize causal mechanisms responsible for anomalous behavior.
发表机构
- Sorbonne Université（索邦大学）
- INSERM（法国国家健康与医学研究院）
- Institut Pierre Louis d’Epidémiologie et de Santé Publique（皮埃尔·路易流行病学与公共卫生研究所）
机构由 AI 辅助整理，请以论文原文为准。
