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10 August 2023 Deep spatial-constraints networks for unsupervised anomaly detection in multivariate time series data
Yanwen Wu, Di Ge, Yuhang Cheng
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Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127481T (2023) https://doi.org/10.1117/12.2689395
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China
Abstract
High-dimensional time series anomaly detection has always been an important challenge in the field of system security. Most existing methods are dedicated to modelling the temporal variation of features to capture anomalous moment points, however as features become more high-dimensional, the associations between features take on a complex spatial structure. This spatial structure information will compensate for the constraints of unsupervised training conditions, and guide the model to be more fully trained. In this study, we propose a detection model that incorporates spatial supervision signals. The model not only simultaneously models the temporal and spatial dependencies, but also simulates the topological structure and physical characteristics of data in the real world through graph structure learning and contrastive learning, providing guidance for anomaly detection. We conducted experiments on two real-world datasets and demonstrated that our model outperforms the baseline. Finally, we conducted detailed data analysis to provide interpretability for the model.
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Yanwen Wu, Di Ge, and Yuhang Cheng "Deep spatial-constraints networks for unsupervised anomaly detection in multivariate time series data", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127481T (10 August 2023); https://doi.org/10.1117/12.2689395
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KEYWORDS
Sensors
Machine learning
Data modeling
Transformers
Performance modeling
Systems modeling
Neural networks
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