Diner: Interpretable Anomaly Detection for Seasonal Time Series in Web Services

Abstract

Monitoring and anomaly detection of key performance indicators (KPIs) are crucial for large Internet companies to maintain the reliability of their Web services. Influenced by human behavior and schedules, the KPIs of Web services typically exhibit seasonal characteristics. These characteristics may be complex as different KPIs exhibit differences in trend, multiple periods, and noise behaviors. However, existing anomaly detection methods typically only model one fixed pattern of seasonal KPIs, which may lead to performance degradation when dealing with diverse seasonal KPIs. In this work, we propose a novel anomaly detection model for seasonal KPIs, Diner , which incorporates multiple interpretable components. It is able to capture the additive and multiplicative trends, multiple periods, and seasonal noise in intricate seasonal KPIs, making it easily adaptable to different types of seasonal KPIs. Additionally, we present a set of evaluation criteria for generic time series anomaly detection tasks, which prove more effective in handling ambiguous manual labels and various anomaly events. Experiments are conducted on three real-world datasets, and the performance Diner surpassed both the statistical baseline and the state-of-the-art deep learning baselines.