ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data
This addresses the challenge of understanding dynamic environments from multiple data sources for applications like weather forecasting and customer behavior analysis, representing an incremental improvement in visualization tools.
The paper tackles the problem of detecting and analyzing concept drifts in multi-source time-series data by proposing a visual analysis approach, resulting in an integrated interface called ConceptExplorer that facilitates exploration and comparison, as verified through three case studies and expert interviews.
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.