Bögl, M., Bors, C., Gschwandtner, T., & Miksch, S. (2018). Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data. In A. Puig & R. Raidou (Eds.), EuroVis 2018 - Posters (pp. 45–47). The Eurographics Association. https://doi.org/10.2312/eurp.20181126
Eurographics / IEEE VGTC Conference on Visualization (EuroVis 2018)
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Event date:
4-Jun-2018 - 8-Jun-2018
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Event place:
Brno, Czech Republic, EU
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Number of Pages:
3
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Publisher:
The Eurographics Association
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Publisher:
The Eurographics Association
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Keywords:
Time series analysis; Visualization theory; concepts and paradigms
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Abstract:
The segmenting and labeling of multivariate time series data is applied in different domains, e.g. activity recognition or sensor states. This involves several steps of (pre-) processing, segmenting, and labeling of time intervals, and visually exploring the results as well as iteratively refining the parameters for all the processing steps. Within these processes different uncertainties are involved and relevant. In this poster we identify and categorize important uncertainties in this problem domain. We discuss challenges for visually communicating these uncertainties throughout the segmenting and labeling process.
en
Project title:
Visual Segmentation and Labeling of Multivariate Time Series: 2850-N31 (Fonds zur Förderung der wissenschaftlichen Forschung (FWF))
