Anomaly Detection of Small Time Series Data Based on Improved Generative Adversarial Networks

Author:

Zeng Biqing1ORCID,Zheng Xinru1ORCID,Wang Zhaohong1ORCID,Hong Peilin1ORCID

Affiliation:

1. School of Software Engineering, South China Normal University, China

Publisher

ACM

Reference30 articles.

1. com.atypon.pdfplus.internal.model.plusxml.impl.AuthorGroup@2159dfe5 . Journal of Software , 2020 , 31 ( 3 ): 22 . (in Chinese) DING X O, YU S J, WANG M X, Anomaly Detection on Industrial Time Series Based on Correlation Analysis[J]. Journal of Software, 2020, 31(3): 22. (in Chinese)

2. A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

3. com.atypon.pdfplus.internal.model.plusxml.impl.AuthorGroup@2ba1b495 Conference on Big Data and Smart Computing. 2020: 71-74 . CHOI Y, LIM H, CHOI H, GAN-based Anomaly Detection and Localization of Multivariate Time Series Data for Power Plant[C]//International Conference on Big Data and Smart Computing. 2020: 71-74.

4. com.atypon.pdfplus.internal.model.plusxml.impl.AuthorGroup@6ea33f85 Attentive recurrent comparators[C]//Proceedings of the 34th International Conference on Machine Learning . Sydney, Australia : JMLR , 2017 : 3173-3181. SHYAM P, GUPTA S, DUKKIPATI A. Attentive recurrent comparators[C]//Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: JMLR, 2017: 3173-3181.

5. Generalizing from a few examples: A survey on few-shot learning[J];ACM Computing Surveys,2020

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