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J. Bernard, E. Dobermann, M. Bögl, M. Röhlig, A. Vögele, J. Kohlhammer:
"Visual-Interactive Segmentation of Multivariate Time Series";
Talk: 7th International Eurovis Workshop on Visual Analytics (EuroVA), Groningen, the Netherlands; 2016-06-06 - 2016-06-07; in: "EuroVA 2016 EuroVis Workshop on Visual Analytics", N. Andrienko, M. Sedlmair (ed.); The Eurographics Association, (2016), ISBN: 978-3-03868-016-1; 5 pages.

Choosing appropriate time series segmentation algorithms and relevant parameter values is a challenging problem. In order to choose meaningful candidates it is important that different segmentation results are comparable. We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data. In our prototype, users can interactively select from a rich set of segmentation algorithm candidates. In an overview visualization, the results of these segmentations can be compared and adjusted with regard to visualizations of raw data. A similarity-preserving colormap further facilitates visual comparison and labeling of segments. We present our prototype and demonstrate how it can ease the choice of winning candidates from a set of results for the segmentation of human motion capture data.

Time series analysis;Time series segmentation;Classifier design and evaluation

http://dx.doi.org/10.2312/eurova.20161121

http://dx.doi.org/10.2312/eurova.20161121

Project Head Silvia Miksch:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)

Project Head Silvia Miksch:
Visuelle Segmentierung und Labeling multivariater Zeitserien