AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series

This paper presents AnoViz, a novel visualization tool of anomalies in multivariate time series, to support domain experts and data scientists in understanding anomalous instances in their systems. AnoViz provides an overall summary of time series as well as detailed visualizations of relevant detected anomalies in both query and stream modes, rendering near real-time visual analysis available. Here, we show that AnoViz streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector.

Authors

• Patara Trirat KAIST
• Youngeun Nam KAIST
• Taeyoon Kim KAIST
• Jae-Gil Lee Korea Advanced Institute of Science and Technology

Keywords

• Anomaly Detection
• Data Visualization
• Time Series
• Visual Analysis
• Web Application