Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection

2025-09-18

AI Summary

Existing multivariate time series anomaly detection methods often assume conditional independence among variables, neglecting time-varying, nonlinear spatiotemporal dependencies, and thus fail to identify “collective anomalies”—patterns where the system deviates anomalously as a whole despite individual variables appearing normal. To address this, we propose a decoupled modeling framework that separately learns temporal dynamics (via a Transformer encoder) and inter-variable spatial dependencies (via Copula-based multivariate likelihood modeling) in a shared latent space, jointly optimized through self-supervised contrastive learning. Our approach explicitly captures dynamic, nonlinear, and high-order dependencies without relying on strong independence assumptions. Evaluated on multiple real-world benchmarks, it achieves significant improvements over state-of-the-art methods—particularly in complex collective anomaly scenarios—demonstrating superior detection accuracy and robustness.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

In this paper, we aim to improve multivariate anomaly detection (AD) by modeling the extit{time-varying non-linear spatio-temporal correlations} found in multivariate time series data . In multivariate time series data, an anomaly may be indicated by the simultaneous deviation of interrelated time series from their expected collective behavior, even when no individual time series exhibits a clearly abnormal pattern on its own. In many existing approaches, time series variables are assumed to be (conditionally) independent, which oversimplifies real-world interactions. Our approach addresses this by modeling joint dependencies in the latent space and decoupling the modeling of extit{marginal distributions, temporal dynamics, and inter-variable dependencies}. We use a transformer encoder to capture temporal patterns, and to model spatial (inter-variable) dependencies, we fit a multi-variate likelihood and a copula. The temporal and the spatial components are trained jointly in a latent space using a self-supervised contrastive learning objective to learn meaningful feature representations to separate normal and anomaly samples.

Problem

• Modeling time-varying non-linear spatio-temporal correlations in multivariate data
• Detecting anomalies through simultaneous deviation of interrelated time series
• Addressing oversimplified independence assumptions in existing anomaly detection approaches

Innovation

• Transformer encoder captures temporal patterns
• Multivariate likelihood and copula model spatial dependencies
• Self-supervised contrastive learning in latent space