关键词: 多元时间序列, 根因分析, 概率生成网络, 图注意力网络, 深度学习

Abstract: Root cause analysis is of critical importance in aiding rapid system recovery, accurately assessing risks, and ensuring production safety. Addressing the limitations of current root cause analysis methods, which struggle to adequately characterize dependencies among different sensors and fail to capture stochastic fluctuations in time series, we propose a novel approach for multi-dimensional time series root cause analysis using a probabilistic generative graph attention network. This method embeds dimension features as Gaussian distribution vectors to represent sensor latent characteristics, effectively capturing random fluctuations in multi-dimensional time series and enhancing model robustness against noise. Simultaneously, a deep probability generative graph attention network is constructed to learn the nonlinear dependencies between dimensions, effectively modeling dependencies within sensor networks. Finally, the root cause analysis is conducted by integrating the network topology causal score and the individual node causal score. The proposed method is experimented on two public datasets and one private dataset, achieving optimal values on certain metrics. Specifically, on the SWaT dataset, improves by 2.2%, 6.3%, and 9.3% on PR@5, MAP@5, and MRR metrics respectively compared to the second-best method. On the WADI dataset, this approach improves by 8.1%, 7.0%, and 10.9% on PR@5, MAP@5, and MRR metrics respectively compared to the second-best method. On the Private dataset, this approach improves by 3.6% and 2.3% on MAP@3 and MRR metrics respectively compared to the second-best method.

Key words: multidimensional time series, root cause analysis, probabilistic generative network, graph attention network, deep learning