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Erschienen in: Business & Information Systems Engineering 1/2024

31.07.2023 | Research Paper

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

verfasst von: Weishan Zhang, Yuqian Wang, Leiming Chen, Yong Yuan, Xingjie Zeng, Liang Xu, Hongwei Zhao

Erschienen in: Business & Information Systems Engineering | Ausgabe 1/2024

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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.

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Metadaten
Titel
Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection
verfasst von
Weishan Zhang
Yuqian Wang
Leiming Chen
Yong Yuan
Xingjie Zeng
Liang Xu
Hongwei Zhao
Publikationsdatum
31.07.2023
Verlag
Springer Fachmedien Wiesbaden
Erschienen in
Business & Information Systems Engineering / Ausgabe 1/2024
Print ISSN: 2363-7005
Elektronische ISSN: 1867-0202
DOI
https://doi.org/10.1007/s12599-023-00825-8

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