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

• URL: http://arxiv.org/abs/2509.15033v1
• Date: Thu, 18 Sep 2025 14:57:55 GMT
• Title: Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection
• Authors: Padmaksha Roy, Almuatazbellah Boker, Lamine Mili,
• Abstract summary: In time series data, an anomaly may be indicated by the simultaneous deviation of interrelated time series.<n>Our approach addresses this by modeling joint dependencies in the latent space.
• Score: 2.893006778402251
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we aim to improve multivariate anomaly detection (AD) by modeling the \textit{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 \textit{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.

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