Cuenca Erick, Sallaberry Arnaud, Wang Florence Y, Poncelet Pascal
IEEE Trans Vis Comput Graph. 2018 Dec;24(12):3160-3173. doi: 10.1109/TVCG.2018.2796591. Epub 2018 Jan 23.
Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e.g., from overview to details). To illustrate our approach, two usage examples are presented.
多个时间序列是指在相同时间间隔内出现的一组多个定量变量。它们存在于医学、金融和制造业等许多领域，用于分析目的。近年来，流图可视化（由主题河流图演变而来）已被广泛用于表示多个时间序列中的时间演变模式。然而，流图以及主题河流图在处理多个时间序列时都存在可扩展性问题。为了解决这个问题，可以将多个时间序列组织成一个层次结构，其中各个时间序列根据它们的相近性进行层次分组。在本文中，我们提出了一种基于流图的新方法来传达多个时间序列的层次结构，以促进对时间演变的探索和比较。基于聚焦+上下文技术，我们的方法允许在不同粒度（例如，从概述到细节）上对时间序列进行探索。为了说明我们的方法，给出了两个使用示例。
