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    Supporting Visual Parameter Analysis of Time Series Segmentation with Correlation Calculations

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    Date
    2018
    Author
    Eichner, Christian
    Schumann, Heidrun
    Tominski, Christian ORCID
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    Abstract
    Parameter 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.
    BibTeX
    @inproceedings {10.2312:eurp.20181124,
    booktitle = {EuroVis 2018 - Posters},
    editor = {Anna Puig and Renata Raidou},
    title = {{Supporting Visual Parameter Analysis of Time Series Segmentation with Correlation Calculations}},
    author = {Eichner, Christian and Schumann, Heidrun and Tominski, Christian},
    year = {2018},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-065-9},
    DOI = {10.2312/eurp.20181124}
    }
    URI
    http://dx.doi.org/10.2312/eurp.20181124
    https://diglib.eg.org:443/handle/10.2312/eurp20181124
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    Eurographics Association copyright © 2013 - 2022 
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    Theme by @mire NV
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