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To ensure undisrupted business, large Internet companies need to closelymonitor various KPIs (e.g., Page Views, number of online users, and number oforders) of its Web applications, to accurately detect anomalies and triggertimely troubleshooting/mitigation. However, anomaly detection for theseseasonal KPIs with various patterns and data quality has been a greatchallenge, especially without labels. In this paper, we proposed Donut, anunsupervised anomaly detection algorithm based on VAE. Thanks to a few of ourkey techniques, Donut greatly outperforms a state-of-arts supervised ensembleapproach and a baseline VAE approach, and its best F-scores range from 0.75 to0.9 for the studied KPIs from a top global Internet company. We come up with anovel KDE interpretation of reconstruction for Donut, making it the firstVAE-based anomaly detection algorithm with solid theoretical explanation.

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