2020/07/30 by Xumeng Wang, Wang, Xumeng, Wei Chen +13
Computer Science · #Data Stream Mining Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #Graphics (cs.GR) #Human-Computer Interaction (cs.HC) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2007.15272
openalex publication_date 2020/07/30 · openalex created_date 2020/08/03 · openalex updated_date 2026/08/01
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.