ANOVIZ: A Visual Inspection Tool of Anomalies in Multivariate Time Series
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Trirat, Patara | ko |
| dc.contributor.author | Nam, Youngeun | ko |
| dc.contributor.author | Kim, Taeyoon | ko |
| dc.contributor.author | Lee, Jae-Gil | ko |
| dc.date.accessioned | 2023-05-10T12:00:26Z | - |
| dc.date.available | 2023-05-10T12:00:26Z | - |
| dc.date.created | 2023-02-18 | - |
| dc.date.issued | 2023-02-12 | - |
| dc.identifier.citation | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 | - |
| dc.identifier.uri | http://hdl.handle.net/10203/306686 | - |
| dc.description.abstract | 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. | - |
| dc.language | English | - |
| dc.publisher | Association for the Advancement of Artificial Intelligence | - |
| dc.title | ANOVIZ: A Visual Inspection Tool of Anomalies in Multivariate Time Series | - |
| dc.type | Conference | - |
| dc.type.rims | CONF | - |
| dc.citation.publicationname | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 | - |
| dc.identifier.conferencecountry | US | - |
| dc.identifier.conferencelocation | Washington, D.C | - |
| dc.contributor.localauthor | Lee, Jae-Gil | - |
| dc.contributor.nonIdAuthor | Kim, Taeyoon | - |
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