Quantifying Uncertainty in Multivariate Time Series Pre-Processing
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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.
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Year of Publication |
2019
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Conference Name |
EuroVis Workshop on Visual Analytics (EuroVA)
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Date Published |
06/2019
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The Eurographics Association
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Conference Location |
Porto, Portugal
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ISBN Number |
978-3-03868-087-1
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