Time-series anomaly detection service at Microsoft

Citations of this article
Mendeley users who have this article in their library.
Get full text


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.




Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., … Zhang, Q. (2019). Time-series anomaly detection service at Microsoft. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 3009–3017). Association for Computing Machinery. https://doi.org/10.1145/3292500.3330680

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free