Anomaly detection in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system operations or manufacturing industry. Previous approaches model the joint distribution without considering the underlying mechanism of multivariate time series, making them complicated and computationally hungry. In this paper, we formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism to generate each variable from its direct causes, whose conditional distribution can be directly estimated from data. In light of the modularity property of causal systems, the original problem is divided into a series of separate low-dimensional anomaly detection problems so that where an anomaly happens can be directly identified. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications, showing its efficacy, robustness, and practical feasibility.
翻译:多变时间序列中的异常探测在监测各种真实世界系统的行为方面发挥着重要作用,例如信息技术系统操作或制造业。以前的做法模拟了联合分布,而没有考虑到多变时间序列的基本机制,使得这些系统复杂和计算饥饿。在本文中,我们从因果角度提出异常探测问题,并将异常情况视为没有遵循生成多变数据的正常因果机制的实例。然后我们提出基于因果关系的异常检测方法,首先从数据中了解因果结构,然后推论一个事件是否与从直接原因产生每个变量的当地因果机制相对异常,而直接原因的因果分配条件可以直接从数据中估算出来。鉴于因果系统模块特性,最初的问题被分为一系列单独的低维异常检测问题,这样就可以直接发现异常情况。我们用模拟和公共数据集来评估我们的方法,并对现实世界的AIOps应用进行案例研究,表明其功效、稳健性和实际可行性。