Abstract
Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.
Originalsprache | englisch |
---|---|
Titel | Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020 |
Seiten | 1-11 |
Seitenumfang | 11 |
ISBN (elektronisch) | 9781728180090 |
DOIs | |
Publikationsstatus | Veröffentlicht - Okt. 2020 |
Veranstaltung | IEEE VIS 2020 - Virtuell, USA / Vereinigte Staaten Dauer: 25 Okt. 2020 → 30 Okt. 2020 http://ieeevis.org/year/2020/welcome |
Publikationsreihe
Name | Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020 |
---|
Konferenz
Konferenz | IEEE VIS 2020 |
---|---|
Kurztitel | VIS 2020 |
Land/Gebiet | USA / Vereinigte Staaten |
Ort | Virtuell |
Zeitraum | 25/10/20 → 30/10/20 |
Internetadresse |
ASJC Scopus subject areas
- Medientechnik
- Modellierung und Simulation
Fields of Expertise
- Information, Communication & Computing