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

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To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

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 (2) __INSTITUTION_13__ Tsinghua University, (2) Alibaba Group) · 2018

Related benchmarks

Task	Dataset	Result
Anomaly Detection	SMD	F1 Score35	375
Time Series Anomaly Detection	GECCO	VUS-ROC0.66	97
Time Series Anomaly Detection	TSB-AD-M	VUS-PR26	83
Multivariate Time Series Anomaly Detection	PSM	Precision—	44
Time Series Anomaly Detection	PSM	Standard-F125	38
Time Series Anomaly Detection	SVDB	AUC-PR19	33
Time Series Anomaly Detection	Daphnet	AUC-PR14	33
Time Series Anomaly Detection	MITDB	AUC-PR3	33
Anomaly Detection	NAB (test)	F1 Score93.5	29
Multivariate Time Series Anomaly Detection	MITDB	VUS-PR12	27