CMMD: Cross-Metric Multi-Dimensional Root Cause Analysis

no code implementations • 30 Mar 2022 • Shifu Yan, Caihua Shan, Wenyi Yang, Bixiong Xu, Dongsheng Li, Lili Qiu, Jie Tong, Qi Zhang

To this end, we propose a cross-metric multi-dimensional root cause analysis method, named CMMD, which consists of two key components: 1) relationship modeling, which utilizes graph neural network (GNN) to model the unknown complex calculation among metrics and aggregation function among dimensions from historical data; 2) root cause localization, which adopts the genetic algorithm to efficiently and effectively dive into the raw data and localize the abnormal dimension(s) once the KPI anomalies are detected.

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

2 code implementations • NeurIPS 2020 • Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang

In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.

Multivariate Time Series Forecasting Time Series

Multivariate Time-series Anomaly Detection via Graph Attention Network

2 code implementations • 4 Sep 2020 • Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang

Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.

Anomaly Detection Graph Attention +3

Time-Series Anomaly Detection Service at Microsoft

3 code implementations • 10 Jun 2019 • Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang

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.

Anomaly Detection Saliency Detection +2