Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications-Reference-Cited by-同舟云学术

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Author:

Xu Haowen¹,Feng Yang²,Chen Jie²,Wang Zhaogang²,Qiao Honglin²,Chen Wenxiao¹,Zhao Nengwen¹,Li Zeyan¹,Bu Jiahao¹,Li Zhihan¹,Liu Ying¹,Zhao Youjian¹,Pei Dan¹

Affiliation:

1. Tsinghua University, Beijing, China

2. Alibaba Group, Hangzhou, China

Funder

Alibaba Innovative Research (AIR)

National Natural Science Foundation of China (NSFC)

Publisher

ACM Press

Reference38 articles.

1. Mennatallah Amer, Markus Goldstein, and Slim Abdennadher. 2013. Enhancing one-class support vector machines for unsupervised anomaly detection Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description. ACM, 8--15.

2. Jinwon An and Sungzoon Cho. 2015. Variational Autoencoder based Anomaly Detection using Reconstruction Probability. Technical Report. SNU Data Mining Center. 1--18 pages.

3. Matthew James Beal. 2003. Variational algorithms for approximate Bayesian inference. University of London London.

4. Christopher M Bishop. 2006. Pattern recognition and machine learning. springer.

5. Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM computing surveys (CSUR) Vol. 41, 3 (2009), 15.

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