# Time-Series Anomaly Detection Service at Microsoft

## TL;DR

This paper presents a real-time time-series anomaly detection service at Microsoft, introducing a novel spectral residual and CNN-based algorithm that outperforms existing methods on multiple datasets.

## Contribution

The paper introduces a new anomaly detection algorithm combining Spectral Residual and CNN, adapted from visual saliency detection, for improved accuracy and efficiency.

## Key findings

- Achieves superior detection accuracy on public datasets.
- Demonstrates effectiveness on Microsoft production data.
- Outperforms state-of-the-art baselines.

## Abstract

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.