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.

本文提出了一种基于VAE的无监督异常检测算法Donut，它能够有效地检测季节性的KPI，并在理论上得到了可靠的解释。通过应用于一家全球互联网公司的数据，Donut表现出比监督学习和基线VAE更好的性能，其最佳F1得分在0.75到0.9之间。

基于变分自编码器的Web应用程序中季节性KPI的无监督异常检测