ConceptExplorer：多源时间序列数据概念漂移的可视化分析
ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data
摘要：
时间序列数据在各种场景中被广泛研究，如天气预报，股票市场，客户行为分析。为了全面了解动态环境，有必要理解来自多个数据源的特征。提出了一种新的视觉分析方法，用于检测和分析多源时间序列中的概念漂移。我们提出了一个视觉检测方案，发现概念漂移从多源时间序列的预测模型的基础上。我们设计了一个漂移水平指数来描述动态，和一个一致性判断模型来判断是否从不同来源的概念漂移是一致的。我们的集成可视化界面ConceptExplorer有助于从多源时间序列数据中对概念和概念漂移进行可视化探索、提取、理解和比较。我们进行了三个案例研究和专家访谈，以验证我们的方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
