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C. Bors, J. Bernard, M. Bögl, T. Gschwandtner, J. Kohlhammer, S. Miksch:
"Quantifying Uncertainty in Multivariate Time Series Pre-Processing";
Talk: 21st EG/VGTC Conference on Visualization (EuroVis 2019), Porto, Portugal (invited); 2019-06-03 - 2019-06-07; in: "EuroVis Workshop on Visual Analytics", T. von Landesberger, C. Turkay (ed.); Proceedings of the 21st EG/VGTC Conference on Visualization (EuroVis 2019), 38-3 (2019), ISSN: 0167-7055; 31 - 35.

In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty into the data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, the uncertainty needs to be quantified initially. We address this challenge by formalizing the quantification of uncertainty for multivariate time series preprocessing. To tackle the large design space, we elaborate key considerations for quantifying and aggregating uncertainty. We provide an example how the quantified uncertainty is used in a multivariate time series pre-processing application to assess the effectiveness of pre-processing steps and adjust the pipeline to minimize the introduction of uncertainty.

Time series analysis; Uncertainty; Visualization theory, concepts and paradigms; Visual analytics; Uncertainty quantification

http://dx.doi.org/10.2312/eurova.20191121

https://publik.tuwien.ac.at/files/publik_280206.pdf

Project Head Silvia Miksch:
Visuelle Segmentierung und Labeling multivariater Zeitserien