Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Nengwen Zhao

Nengwen Zhao

Tsinghua University

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


Feb 12, 2018
Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, Honglin Qiao

Figure 1 for Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Figure 2 for Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Figure 3 for Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Figure 4 for Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
* 12 pages (including references), 17 figures, submitted to WWW 2018: The 2018 Web Conference, April 23--27, 2018, Lyon, France. The contents discarded from the conference version due to the 9-page limitation are also included in this version 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email