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dc.contributor.authorNguyen, Bao Dien Quocen_US
dc.contributor.authorHewett, Rattikornen_US
dc.contributor.authorDang, Tommyen_US
dc.contributor.editorTurkay, Cagatay and Vrotsou, Katerinaen_US
dc.description.abstractIn this paper, we propose an analytical approach to automatically extract visual features from doubly time series capturing the unusual associations which are not otherwise possible by investigating individual time series alone. We have extended the visual measures for 2D scatterplots, incorporated univariate time series analysis, and proposed new visual features for doubly time series plots. These measures are discussed and demonstrated via visual examples to clarify their implications and their effectiveness. The results show that distributions, trend, shape, noise, among other characteristics, can be used to uncover the latent features and events in temporal datasets.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.titleCongnostics: Visual Features for Doubly Time Series Plotsen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.description.sectionheadersVisual Analysis of High Dimensional and Temporal Data

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License