论文标题
通过健壮的时间序列分解对季节性指标的异常检测
Anomaly Detection on Seasonal Metrics via Robust Time Series Decomposition
论文作者
论文摘要
Web服务的稳定性和持久性对于互联网公司而言至关重要，以改善用户体验和业务性能。为了注重众多指标并报告异常情况，公司和机构的各个部门开发和应用了时间序列异常检测方法。在本文中，我们提出了一种强大的异常检测算法（MEDIFF），以实时监视在线业务指标。具体而言，使用稳健统计度量的分解方法用于时间序列，以使趋势和季节性成分脱离。随着日光节省时间（DST）偏移和假期的影响，时间序列分解了相应的组件。通过广义统计方法测试了分解后的残差，以检测时间序列中的异常值。我们通过使用我们标记的内部业务指标比较了拟议的Mediff算法和两种开源算法（SH-ESD和Donut）。结果证明了拟议的Mediff算法的有效性。
The stability and persistence of web services are important to Internet companies to improve user experience and business performances. To keep eyes on numerous metrics and report abnormal situations, time series anomaly detection methods are developed and applied by various departments in companies and institutions. In this paper, we proposed a robust anomaly detection algorithm (MEDIFF) to monitor online business metrics in real time. Specifically, a decomposition method using robust statistical metric--median--of the time series was applied to decouple the trend and seasonal components. With the effects of daylight saving time (DST) shift and holidays, corresponding components were decomposed from the time series. The residual after decomposition was tested by a generalized statistics method to detect outliers in the time series. We compared the proposed MEDIFF algorithm with two open source algorithms (SH-ESD and DONUT) by using our labeled internal business metrics. The results demonstrated the effectiveness of the proposed MEDIFF algorithm.
