Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection

Weishan Zhang (), Yuqian Wang, Leiming Chen, Yong Yuan, Xingjie Zeng, Liang Xu and Hongwei Zhao
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Weishan Zhang: China University of Petroleum (East China)
Yuqian Wang: China University of Petroleum (East China)
Leiming Chen: China University of Petroleum (East China)
Yong Yuan: Renmin University of China
Xingjie Zeng: China University of Petroleum (East China)
Liang Xu: Beijing University of Science and Technology
Hongwei Zhao: China University of Petroleum (East China)

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2024, vol. 66, issue 1, No 3, 19-42

Abstract: Abstract Multivariate time-series data exhibit intricate correlations in both temporal and spatial dimensions. However, existing network architectures often overlook dependencies in the spatial dimension and struggle to strike a balance between long-term and short-term patterns when extracting features from the data. Furthermore, industries within the business community are hesitant to share their raw data, which hinders anomaly prediction accuracy and detection performance. To address these challenges, the authors propose a dynamic circular network-based federated dual-view learning approach. Experimental results from four open-source datasets demonstrate that the method outperforms existing methods in terms of accuracy, recall, and F1_score for anomaly detection.

Keywords: Multivariate time series; Federated learning; Graph neural network; Anomaly detection; Deep learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12599-023-00825-8

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