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).
Erick Cuenca, Arnaud Sallaberry, Florence Y. Wang, and Pascal Poncelet. MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series. IEEE Transactions on Visualization and Computer Graphics, 24(12):3160-3173, 2018. doi: 10.1109/TVCG.2018.2796591
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