  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.
