LSTM-Based VAE-GAN for Time-Series Anomaly Detection
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3738; https://doi.org/10.3390/s20133738
Received: 19 May 2020
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Revised: 23 June 2020
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Accepted: 29 June 2020
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Published: 3 July 2020
(This article belongs to the Special Issue Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis)
Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. However, this method needs to find the best mapping from real-time space to the latent space at the anomaly detection stage, which brings new errors and takes a long time. In this paper, we propose a long short-term memory-based variational autoencoder generation adversarial networks (LSTM-based VAE-GAN) method for time series anomaly detection, which effectively solves the above problems. Our method jointly trains the encoder, the generator and the discriminator to take advantage of the mapping ability of the encoder and the discrimination ability of the discriminator simultaneously. The long short-term memory (LSTM) networks are used as the encoder, the generator and the discriminator. At the anomaly detection stage, anomalies are detected based on reconstruction difference and discrimination results. Experimental results show that the proposed method can quickly and accurately detect anomalies.
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Keywords:
anomaly detection; VAE-GAN; time series
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MDPI and ACS Style
Niu, Z.; Yu, K.; Wu, X. LSTM-Based VAE-GAN for Time-Series Anomaly Detection. Sensors 2020, 20, 3738. https://doi.org/10.3390/s20133738
AMA Style
Niu Z, Yu K, Wu X. LSTM-Based VAE-GAN for Time-Series Anomaly Detection. Sensors. 2020; 20(13):3738. https://doi.org/10.3390/s20133738
Chicago/Turabian StyleNiu, Zijian, Ke Yu, and Xiaofei Wu. 2020. "LSTM-Based VAE-GAN for Time-Series Anomaly Detection" Sensors 20, no. 13: 3738. https://doi.org/10.3390/s20133738
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