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DC poleHodnotaJazyk
dc.contributor.authorCibulski, Lena
dc.contributor.authorMay, Thorsten
dc.contributor.authorPreim, Bernhard
dc.contributor.authorBernard, Jürgen
dc.contributor.authorKohlhammer, Jörn
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-10-22T05:58:51Z
dc.date.available2019-10-22T05:58:51Z
dc.date.issued2019
dc.identifier.citationJournal of WSCG. 2019, vol. 27, no. 2, p. 93-102.en
dc.identifier.issn1213-6964 (on-line)
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.urihttp://hdl.handle.net/11025/35593
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectvizuální analytikacs
dc.subjectvýběr funkcecs
dc.subjectkonzistencecs
dc.subjectvícerozměrné časové řadycs
dc.subjectmodel-agnosticcs
dc.subjectpředpovídánícs
dc.titleVisualizing Time Series Consistency for Feature Selectionen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedFeature 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.en
dc.subject.translatedvisual analyticsen
dc.subject.translatedfeature selectionen
dc.subject.translatedconsistencyen
dc.subject.translatedmultivariate time seriesen
dc.subject.translatedmodel-agnosticen
dc.subject.translatedforecastingen
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2019.27.2.2
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 27, Number 2 (2019)

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Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/35593

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 27 (2019)
  6. Volume 27, Number 2 (2019)