Supporting Visual Parameter Analysis of Time Series Segmentation with Correlation Calculations

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dc.contributor.authorEichner, Christianen_US
dc.contributor.authorSchumann, Heidrunen_US
dc.contributor.authorTominski, Christianen_US
dc.contributor.editorAnna Puig and Renata Raidouen_US
dc.date.accessioned2018-06-02T17:55:50Z
dc.date.available2018-06-02T17:55:50Z
dc.date.issued2018
dc.description.abstractParameter analysis can be used to find out how individual parameters influence the output of an algorithm. We aim to support the visual parameter analysis of algorithms for the segmentation of time series. To this end, we automatically search for correlations between parameters and the segmentation outputs. Correlations are not only determined globally, but also locally within parameter subspaces. Calculated correlations are used to visually emphasize parameter and value ranges with high influence on the segmentation. By interactive exploration, the analyst can study the multidimensional parameter space in depth.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2018 - Posters
dc.identifier.doi10.2312/eurp.20181124
dc.identifier.isbn978-3-03868-065-9
dc.identifier.pages37-39
dc.identifier.urihttps://doi.org/10.2312/eurp.20181124
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurp20181124
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectVisualization application domains
dc.subjectInformation visualization
dc.subjectMathematics of computing
dc.subjectTime series analysis
dc.titleSupporting Visual Parameter Analysis of Time Series Segmentation with Correlation Calculationsen_US
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