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Methods for Multivariate Time Series Anomaly Detection (MTSAD) have attracted much interest among the research community, with the number of proposed methodologies exploding in the last six years. Existing reviews and surveys have categorized MTSAD methods into conventional, machine learning and deep neural network (DNN)-based approaches, further distinguishing DNN-based methods by architecture and their focus on Temporal (T), Spatial (S) or Spatio-Temporal (ST) modeling. However, there are still aspects that require further exploration, for example how the spatial and temporal dependencies are organized in ST models. This article proposes a novel characterization of the Temporal/Spatial (T/S) dimension in six categories defined based on the analysis of 177 Scopus-indexed documents on MTSAD obtained through a search on this database between 2019-2024. As the first outcome of a large-scale review of methodologies, this study identifies emerging trends and opens new research directions for future research in MTSAD methodologies.

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Towards a New Categorization of Models for Multivariate Time Series Anomaly Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Bruna Alves,
  • Armando J. Pinho,
  • Sónia Gouveia

摘要

Methods for Multivariate Time Series Anomaly Detection (MTSAD) have attracted much interest among the research community, with the number of proposed methodologies exploding in the last six years. Existing reviews and surveys have categorized MTSAD methods into conventional, machine learning and deep neural network (DNN)-based approaches, further distinguishing DNN-based methods by architecture and their focus on Temporal (T), Spatial (S) or Spatio-Temporal (ST) modeling. However, there are still aspects that require further exploration, for example how the spatial and temporal dependencies are organized in ST models. This article proposes a novel characterization of the Temporal/Spatial (T/S) dimension in six categories defined based on the analysis of 177 Scopus-indexed documents on MTSAD obtained through a search on this database between 2019-2024. As the first outcome of a large-scale review of methodologies, this study identifies emerging trends and opens new research directions for future research in MTSAD methodologies.