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Published 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

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

Published in: Business & Information Systems Engineering | Issue 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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Metadata
Title
Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection
Authors
Weishan Zhang
Yuqian Wang
Leiming Chen
Yong Yuan
Xingjie Zeng
Liang Xu
Hongwei Zhao
Publication date
31-07-2023
Publisher
Springer Fachmedien Wiesbaden
Published in
Business & Information Systems Engineering / Issue 1/2024
Print ISSN: 2363-7005
Electronic ISSN: 1867-0202
DOI
https://doi.org/10.1007/s12599-023-00825-8

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