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Quantifying Uncertainty in Multivariate Time Series Pre-Processing

: Bors, Christian; Bernard, Jürgen; Bögl, Markus; Gschwandtner, Theresia; Kohlhammer, Jörn; Miksch, Silvia


Landesberger, Tatiana von (Program Chair); Kohlhammer, Jörn (Steering Committee); Fellner, Dieter W. (Proceedings Production Ed.) ; European Association for Computer Graphics -EUROGRAPHICS-:
EuroVA 2019, EuroVis Workshop on Visual Analytics : Porto, Portugal, June 3, 2019
Aire-la-Ville: Eurographics Association, 2019
ISBN: 978-3-03868-087-1
Workshop on Visual Analytics (EuroVA) <10, 2019, Porto>
Conference Paper
Fraunhofer IGD ()
Lead Topic: Visual Computing as a Service; Research Line: Computer graphics (CG); Research Line: Modeling (MOD); multivariate time series; uncertainty visualization; Visual analytics

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.