Quantifying Uncertainty in Multivariate Time Series Pre-Processing

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An uncertainty quantification cube for multivariate time series
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Conference Paper
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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.
Year of Publication
2019
Conference Name
EuroVis Workshop on Visual Analytics (EuroVA)
Date Published
06/2019
Publisher
The Eurographics Association
Conference Location
Porto, Portugal
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