{"ID":2869393,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15033","arxiv_id":"2509.15033","title":"Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection","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.","short_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 e...","url_abs":"https://arxiv.org/abs/2509.15033","url_pdf":"https://arxiv.org/pdf/2509.15033v1","authors":"[\"Padmaksha Roy\",\"Almuatazbellah Boker\",\"Lamine Mili\"]","published":"2025-09-18T14:57:55Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}