ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection

Tao Yin (Chongqing University) · Shaochen Fu (Chongqing University) · Zhibin Zhang (Chongqing University) · Li Huang (Chongqing University) · Xiaohong Zhang (Chongqing University) · Yiyuan Yang (Department of Computer Science, University of Oxford) · Kaixiang Yang (Huazhong University of Science and Technology) · Meng Yan (Chongqing University)

anomalous dispersionconditional mutual informationcontrastive fusion mechanismcross-view consistency discriminative representations high-dimensional spacesinductive signalmean pairwise distancemean squared error minimizationmultivariate data representation learningscattering phenomenonspatio-temporal couplingstemporal encodertime series anomaly detectiontopological encoder

One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, as traditional anomaly detection methods focus on modeling spatial or temporal dependencies independently, resulting in suboptimal representation learning and limited sensitivity to anomalous dispersion in high-dimensional spaces. In this work, we conduct an empirical analysis showing that both normal and anomalous samples tend to scatter in high-dimensional space, especially anomalous samples are markedly more dispersed. We formalize this dispersion phenomenon as scattering, quantified by the mean pairwise distance among sample representations, and leverage it as an inductive signal to enhance spatio-temporal anomaly detection. Technically, we propose ScatterAD to model representation scattering across temporal and topological dimensions. ScatterAD incorporates a topological encoder for capturing graph-structured scattering and a temporal encoder for constraining over-scattering through mean squared error minimization between neighboring time steps. We introduce a contrastive fusion mechanism to ensure the complementarity of the learned temporal and topological representations. Additionally, we theoretically show that maximizing the conditional mutual information between temporal and topological views improves cross-view consistency and enhances more discriminative representations. Extensive experiments on multiple public benchmarks show that ScatterAD achieves state-of-the-art performance on multivariate time series anomaly detection.