@conference{487,
  author = {Christian Bors and Jürgen Bernard and Markus Bögl and Theresia Gschwandtner and Jörn Kohlhammer and Silvia Miksch},
  title = {Quantifying Uncertainty in Multivariate Time Series Pre-Processing},
  abstract = {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 pre-processing. 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.},
  year = {2019},
  journal = {EuroVis Workshop on Visual Analytics (EuroVA)},
  month = {06/2019},
  publisher = {The Eurographics Association},
  address = {Porto, Portugal},
  isbn = {978-3-03868-087-1},
  doi = {10.2312/eurova.20191121},
}