CNTS: Cooperative Network for Time Series

The use of deep learning techniques in detecting anomalies in time series data has been an active area of research with a long history of development and a variety of approaches. In particular, reconstruction-based unsupervised anomaly detection methods have gained popularity due to their intuitive...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.31941-31950
Main Authors: Yang, Jinsheng, Shao, Yuanhai, Li, Chun-Na
Format: Article
Language:English
Subjects:
Anomalies
Anomaly detection
cooperative network
Cooperative systems
Data models
Deep learning
Detectors
Multiple objective analysis
Optimization
Outliers (statistics)
Reconstruction
Sensors
Source code
Task analysis
Time series
Time series analysis
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Summary:The use of deep learning techniques in detecting anomalies in time series data has been an active area of research with a long history of development and a variety of approaches. In particular, reconstruction-based unsupervised anomaly detection methods have gained popularity due to their intuitive assumptions and low computational requirements. However, these methods are often susceptible to outliers and do not effectively model anomalies, leading to suboptimal results. This paper presents a novel approach for unsupervised anomaly detection, called the Cooperative Network Time Series (CNTS) approach. The CNTS system consists of two components: a detector and a reconstructor. The detector is responsible for directly detecting anomalies, while the reconstructor provides reconstruction information to the detector and updates its learning based on anomalous information received from the detector. The central aspect of CNTS is a multi-objective optimization problem, which is solved through a cooperative solution strategy. Experiments on three real-world datasets demonstrate the state-of-the-art performance of CNTS and confirm the effectiveness of the detector and reconstructor. The source code for this study is publicly available on GitHub ( https://github.com/BomBooooo/CNTS/tree/main ).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3259467