标题： 动态系统中可解释的异常检测与根本原因分析的统一 显示英文标题

标题： Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems

摘要： 动力系统在各种科学和工程领域中很常见，容易出现异常，这可能显著影响其性能和可靠性。 本文解决了由常微分方程(ODEs)控制的动力系统中的关键挑战：异常检测、根本原因定位和异常类型分类。 我们定义了两种类型的异常：网络异常，这些异常通过相互连接的变量传播；测量异常，这些异常局限于单个变量。 为了解决这些挑战，我们提出了可解释的因果常微分方程(ICODE)网络，这是一种模型内生的可解释学习框架。 ICODE利用神经ODE进行异常检测，同时通过解释通道进行因果推理以执行根本原因分析(RCA)，阐明为什么特定时间段被标记为异常。 ICODE设计用于在一个可解释的框架中同时执行异常检测、RCA和异常类型分类。 我们的方法基于一个假设，即异常会改变系统的底层ODEs，表现为变量之间因果关系的变化。 我们提供了理论分析，说明如何利用学习模型参数的扰动来识别时间序列数据中的异常及其根本原因。 全面的实验评估证明了ICODE在各种动力系统中的有效性，展示了其准确检测异常、分类异常类型并确定其来源的能力。 显示英文摘要

摘要： Dynamical systems, prevalent in various scientific and engineering domains, are susceptible to anomalies that can significantly impact their performance and reliability. This paper addresses the critical challenges of anomaly detection, root cause localization, and anomaly type classification in dynamical systems governed by ordinary differential equations (ODEs). We define two categories of anomalies: cyber anomalies, which propagate through interconnected variables, and measurement anomalies, which remain localized to individual variables. To address these challenges, we propose the Interpretable Causality Ordinary Differential Equation (ICODE) Networks, a model-intrinsic explainable learning framework. ICODE leverages Neural ODEs for anomaly detection while employing causality inference through an explanation channel to perform root cause analysis (RCA), elucidating why specific time periods are flagged as anomalous. ICODE is designed to simultaneously perform anomaly detection, RCA, and anomaly type classification within a single, interpretable framework. Our approach is grounded in the hypothesis that anomalies alter the underlying ODEs of the system, manifesting as changes in causal relationships between variables. We provide a theoretical analysis of how perturbations in learned model parameters can be utilized to identify anomalies and their root causes in time series data. Comprehensive experimental evaluations demonstrate the efficacy of ICODE across various dynamical systems, showcasing its ability to accurately detect anomalies, classify their types, and pinpoint their origins.