Bors, Christian ; Bernard, Jürgen ; Bögl, Markus ; Gschwandtner, Theresia ; Kohlhammer, Jörn ; Miksch, Silvia (2019)
Quantifying Uncertainty in Multivariate Time Series Pre-Processing.
International EuroVis Workshop on Visual Analytics (EuroVA). Porto, Portugal (03.06.2019-03.06.2019)
doi: 10.2312/eurova.20191121
Conference or Workshop Item, Bibliographie
Abstract
In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty intothe data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, the uncertainty needs to bequantified 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. Weprovide an example how the quantified uncertainty is used in a multivariate time series pre-processing application to assess theeffectiveness of pre-processing steps and adjust the pipeline to minimize the introduction of uncertainty.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2019 |
| Creators: | Bors, Christian ; Bernard, Jürgen ; Bögl, Markus ; Gschwandtner, Theresia ; Kohlhammer, Jörn ; Miksch, Silvia |
| Type of entry: | Bibliographie |
| Title: | Quantifying Uncertainty in Multivariate Time Series Pre-Processing |
| Language: | English |
| Date: | 2019 |
| Event Title: | International EuroVis Workshop on Visual Analytics (EuroVA) |
| Event Location: | Porto, Portugal |
| Event Dates: | 03.06.2019-03.06.2019 |
| DOI: | 10.2312/eurova.20191121 |
| URL / URN: | https://www.eurova.org/ |
| Abstract: | In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty intothe data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, the uncertainty needs to bequantified 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. Weprovide an example how the quantified uncertainty is used in a multivariate time series pre-processing application to assess theeffectiveness of pre-processing steps and adjust the pipeline to minimize the introduction of uncertainty. |
| Uncontrolled Keywords: | Multivariate time series Uncertainty visualization Visual analytics |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 09 Apr 2020 10:52 |
| Last Modified: | 09 Apr 2020 10:52 |
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