通过差异图发现定位时间序列异常的根源变量
Root cause analysis via difference graph discovery from linear time-series data
- 基于线性动态因果模型，识别正常与异常状态间的因果系数变化
- 在仿真与真实运维、医疗数据上验证了方法的有效性
- 适合需要精准定位系统异常根源的研究者和工程师
根因分析旨在识别复杂动态系统中异常行为的成因。本文从差异图发现的角度研究线性时间序列中的根因分析问题，重点关注效应反转型根因——即在正常与异常状态下因果系数发生变化的变量。我们基于线性离散时间动态结构因果模型形式化该问题，并将原本用于两组人群差异图发现的方法适配至时间序列场景，其中两组人群被替换为正常与异常状态。首先在模拟数据上评估所提方法，随后在真实的IT监控与重症监护监测数据集上展示其实际应用价值。结果表明，差异图发现能够有效定位导致异常行为的因果机制。
原文摘要 · Abstract (English)
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.
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