Title:	Visualizing Time Series Consistency for Feature Selection
Authors:	Cibulski, Lena May, Thorsten Preim, Bernhard Bernard, Jürgen Kohlhammer, Jörn
Citation:	Journal of WSCG. 2019, vol. 27, no. 2, p. 93-102.
Issue Date:	2019
Publisher:	Václav Skala - UNION Agency
Document type:	článek article
URI:	http://hdl.handle.net/11025/35593
ISSN:	1213-6964 (on-line) 1213-6972 (print) 1213-6980 (CD-ROM)
Keywords:	vizuální analytika;výběr funkce;konzistence;vícerozměrné časové řady;model-agnostic;předpovídání
Keywords in different language:	visual analytics;feature selection;consistency;multivariate time series;model-agnostic;forecasting
Abstract in different language:	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 alsodesirable 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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	Volume 27, Number 2 (2019)

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