Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Time series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing Visual Analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity.
Description
@inproceedings{10.2312:evs.20201040,
booktitle = {EuroVis 2020 - Short Papers},
editor = {Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta},
title = {{Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques}},
author = {Bors, Christian and Eichner, Christian and Miksch, Silvia and Tominski, Christian and Schumann, Heidrun and Gschwandtner, Theresia},
year = {2020},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-106-9},
DOI = {10.2312/evs.20201040}
}