Cibulski, Lena ; May, Thorsten ; Preim, Bernhard ; Bernard, Jürgen ; Kohlhammer, Jörn (2019)
Visualizing Time Series Consistency for Feature Selection.
In: Journal of WSCG, 27 (2)
doi: 10.24132/JWSCG.2019.27.2.2
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Feature selection is an effective technique to reduce dimensionality, for example when the condition of a system is to be understood from multivariate observations. The selection of variables often involves a priori assumptions about underlying phenomena. To avoid the associated uncertainty, we aim at a selection criterion that only considers the observations. For nominal data, consistency criteria meet this requirement: a variable subset is consistent, if no observations with equal values on the subset have different output values. Such a model-agnostic criterion is also desirable for forecasting. However, consistency has not yet been applied to multivariate time series. In this work, we propose a visual consistency-based technique for analyzing a time series subset’s discriminating ability w.r.t. characteristics of an output variable. An overview visualization conveys the consistency of output progressions associated with comparable observations. Interaction concepts and detail visualizations provide a steering mechanism towards inconsistencies. We demonstrate the technique’s applicability based on two real-world scenarios. The results indicate that the technique is open to any forecasting task that involves multivariate time series, because analysts could assess the combined discriminating ability without any knowledge about underlying phenomena.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Cibulski, Lena ; May, Thorsten ; Preim, Bernhard ; Bernard, Jürgen ; Kohlhammer, Jörn |
| Art des Eintrags: | Bibliographie |
| Titel: | Visualizing Time Series Consistency for Feature Selection |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of WSCG |
| Jahrgang/Volume einer Zeitschrift: | 27 |
| (Heft-)Nummer: | 2 |
| DOI: | 10.24132/JWSCG.2019.27.2.2 |
| Kurzbeschreibung (Abstract): | Feature selection is an effective technique to reduce dimensionality, for example when the condition of a system is to be understood from multivariate observations. The selection of variables often involves a priori assumptions about underlying phenomena. To avoid the associated uncertainty, we aim at a selection criterion that only considers the observations. For nominal data, consistency criteria meet this requirement: a variable subset is consistent, if no observations with equal values on the subset have different output values. Such a model-agnostic criterion is also desirable for forecasting. However, consistency has not yet been applied to multivariate time series. In this work, we propose a visual consistency-based technique for analyzing a time series subset’s discriminating ability w.r.t. characteristics of an output variable. An overview visualization conveys the consistency of output progressions associated with comparable observations. Interaction concepts and detail visualizations provide a steering mechanism towards inconsistencies. We demonstrate the technique’s applicability based on two real-world scenarios. The results indicate that the technique is open to any forecasting task that involves multivariate time series, because analysts could assess the combined discriminating ability without any knowledge about underlying phenomena. |
| Freie Schlagworte: | Visual analytics Feature selection Consistency Multivariate time series |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 09 Apr 2020 09:51 |
| Letzte Änderung: | 09 Apr 2020 09:51 |
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