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In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs).
Recurrence plots of dynamical systems
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Time-series novelty detection using one-class support vector machines. In Proceedings of the International Joint Conference on Neural Networks, 2003. , Vol. 3. IEEE, 1741–1745
Junshui Ma and Simon Perkins. 2003 · 2003
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Evaluating Real-Time Anomaly Detection Algorithms–The Numenta Anomaly Benchmark. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) . IEEE, 38–44
Alexander Lavin and Subutai Ahmad. 2015 · 2015
Earlier work this paper cites.
Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Workshops at the twenty-ninth AAAI conference on artificial intelligence , Vol. 1
Zhiguang Wang and Tim Oates. 2015 · 2015
Earlier work this paper cites.
Automatic anomaly detection in the cloud via statistical learning
Jordan Hochenbaum, Owen S Vallis, and Arun Kejariwal. 2017 · 2017
Cited alongside, same era.
Introvae: Introspective variational autoencoders for photographic image synthesis. In Advances in neural information processing systems . 52–63
Huaibo Huang, Ran He, Zhenan Sun, Tieniu Tan, et al · 2018
Cited alongside, same era.
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 387–395
Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Soderstrom. 2018 · 2018
Cited alongside, same era.
Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. In Proceedings of the 2018 World Wide Web Conference . 187–196
Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et al · 2018
Cited alongside, same era.
Time-Series Anomaly Detection Service at Microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 3009–3017
Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang. 2019 · 2019
Later among the works it cites.
Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2828–2837
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei. 2019 · 2019
Later among the works it cites.
RobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks
Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, and Huan Xu. 2020 · 2020
Later among the works it cites.
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2020 · 2020
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MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In International Conference on Artificial Neural Networks . Springer, 703–716
Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, and See-Kiong Ng. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz. 2020 · 2020
Later among the works it cites.