DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection
In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vas...
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| Veröffentlicht in: | Alexandria engineering journal Jg. 108; S. 436 - 444 |
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Elsevier B.V
01.12.2024
Elsevier |
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| ISSN: | 1110-0168 |
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| Abstract | In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model’s capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance. |
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| AbstractList | In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model’s capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance. |
| Author | Liu, Peng Garg, Sahil Mahmoudi, Saïd Zhao, Xigang Kaddoum, Georges Hassan, Mohammad Mehedi |
| Author_xml | – sequence: 1 givenname: Xigang surname: Zhao fullname: Zhao, Xigang organization: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China – sequence: 2 givenname: Peng orcidid: 0000-0002-3403-2604 surname: Liu fullname: Liu, Peng email: perryliu@hdu.edu.cn organization: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China – sequence: 3 givenname: Saïd orcidid: 0000-0001-8272-9425 surname: Mahmoudi fullname: Mahmoudi, Saïd organization: Computer Science Department, Faculty of Engineering, University of Mons, Mons, 7000, Belgium – sequence: 4 givenname: Sahil surname: Garg fullname: Garg, Sahil organization: École de technologie supérieure, Montreal, H3C 1K3, Canada – sequence: 5 givenname: Georges orcidid: 0000-0002-5025-6624 surname: Kaddoum fullname: Kaddoum, Georges organization: École de technologie supérieure, Montreal, H3C 1K3, Canada – sequence: 6 givenname: Mohammad Mehedi surname: Hassan fullname: Hassan, Mohammad Mehedi organization: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia |
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| Copyright | 2024 Faculty of Engineering, Alexandria University |
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| Keywords | Normalizing flow Autoencoder Anomaly detection Time series |
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