Bors, ChristianChristianBorsBernard, JürgenJürgenBernardBögl, MarkusMarkusBöglGschwandtner, TheresiaTheresiaGschwandtnerKohlhammer, JörnJörnKohlhammerMiksch, SilviaSilviaMiksch2022-03-142022-03-142019https://publica.fraunhofer.de/handle/publica/40673310.2312/eurova.20191121In 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.enLead Topic: Visual Computing as a ServiceResearch Line: Computer graphics (CG)Research Line: Modeling (MOD)multivariate time seriesuncertainty visualizationVisual analytics006conference paper