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Found 4 papers, 3 papers with code
CMMD: Cross-Metric Multi-Dimensional Root Cause Analysis
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
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Graph Neural Network
Multivariate Time Series Forecasting
+1
Multivariate Time-series Anomaly Detection via Graph Attention Network
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
Ranked #6 on
Unsupervised Anomaly Detection
on SMAP
Time-Series Anomaly Detection Service at Microsoft
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.
