  Large companies need to monitor various metrics (for example, Page Views and
Revenue) of their applications and services in real time. At Microsoft, we
develop a time-series anomaly detection service which helps customers to
monitor the time-series continuously and alert for potential incidents on time.
In this paper, we introduce the pipeline and algorithm of our anomaly detection
service, which is designed to be accurate, efficient and general. The pipeline
consists of three major modules, including data ingestion, experimentation
platform and online compute. To tackle the problem of time-series anomaly
detection, we propose a novel algorithm based on Spectral Residual (SR) and
Convolutional Neural Network (CNN). Our work is the first attempt to borrow the
SR model from visual saliency detection domain to time-series anomaly
detection. Moreover, we innovatively combine SR and CNN together to improve the
performance of SR model. Our approach achieves superior experimental results
compared with state-of-the-art baselines on both public datasets and Microsoft
production data.
