Název:	Visualizing Time Series Consistency for Feature Selection
Autoři:	Cibulski, Lena May, Thorsten Preim, Bernhard Bernard, Jürgen Kohlhammer, Jörn
Citace zdrojového dokumentu:	Journal of WSCG. 2019, vol. 27, no. 2, p. 93-102.
Datum vydání:	2019
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	článek article
URI:	http://hdl.handle.net/11025/35593
ISSN:	1213-6964 (on-line) 1213-6972 (print) 1213-6980 (CD-ROM)
Klíčová slova:	vizuální analytika;výběr funkce;konzistence;vícerozměrné časové řady;model-agnostic;předpovídání
Klíčová slova v dalším jazyce:	visual analytics;feature selection;consistency;multivariate time series;model-agnostic;forecasting
Abstrakt v dalším jazyce:	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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	Volume 27, Number 2 (2019)

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.