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
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Bors, Christian ; Bernard, Jürgen ; Bögl, Markus ; Gschwandtner, Theresia ; Kohlhammer, Jörn ; Miksch, Silvia |
| Art des Eintrags: | Bibliographie |
| Titel: | Quantifying Uncertainty in Multivariate Time Series Pre-Processing |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Veranstaltungstitel: | International EuroVis Workshop on Visual Analytics (EuroVA) |
| Veranstaltungsort: | Porto, Portugal |
| Veranstaltungsdatum: | 03.06.2019-03.06.2019 |
| DOI: | 10.2312/eurova.20191121 |
| URL / URN: | https://www.eurova.org/ |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Multivariate time series Uncertainty visualization Visual analytics |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 09 Apr 2020 10:52 |
| Letzte Änderung: | 09 Apr 2020 10:52 |
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