Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908545848442880 |
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| author | Roy, Padmaksha Boker, Almuatazbellah Mili, Lamine |
| author_facet | Roy, Padmaksha Boker, Almuatazbellah Mili, Lamine |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15033 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection Roy, Padmaksha Boker, Almuatazbellah Mili, Lamine Machine Learning 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. |
| title | Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.15033 |