Quantifying Uncertainty in Multivariate Time Series Pre-Processing
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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 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.
Description
@inproceedings{10.2312:eurova.20191121,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Landesberger, Tatiana von and Turkay, Cagatay},
title = {{Quantifying Uncertainty in Multivariate Time Series Pre-Processing}},
author = {Bors, Christian and Bernard, Jürgen and Bögl, Markus and Gschwandtner, Theresia and Kohlhammer, Jörn and Miksch, Silvia},
year = {2019},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-087-1},
DOI = {10.2312/eurova.20191121}
}