Papers › AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series
AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series
Patara Trirat, Youngeun Nam, Taeyoon Kim, Jae-Gil Lee
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections