The MultiStream Implementation: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Software Year : 2019

The MultiStream Implementation: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series

Abstract

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). This code is the implementation of Mutlistream that have been used in: E. Cuenca, A. Sallaberry, F. Y. Wang, and P. Poncelet. "MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series". In IEEE Transactions on Visualization and Computer Graphics, Vol. 24, N. 12, 2018, pp. 3160-3173.

Cite

Erick Cuenca Pauta, Arnaud Sallaberry, Florence Ying Wang, Pascal Poncelet. The MultiStream Implementation: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series. 2019, ⟨swh:1:dir:c503b02ba79ebef55b413c592f7bf725ddccb29b;origin=https://hal.archives-ouvertes.fr/lirmm-02138960;visit=swh:1:snp:14cacd7e195b345d6fb25518a026cb322f1b99f7;anchor=swh:1:rev:9c712b79fe002d2a93f4f8374231b6dec5c43259;path=/⟩. ⟨lirmm-02138960⟩
117 View
3 Download

Share

Gmail Facebook X LinkedIn More