Congnostics: Visual Features for Doubly Time Series Plots

Simple item page
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.date.accessioned2020-05-24T13:31:31Z
dc.date.available2020-05-24T13:31:31Z
dc.date.issued2020
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.description.sectionheadersVisual Analysis of High Dimensional and Temporal Data
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20201086
dc.identifier.isbn978-3-03868-116-8
dc.identifier.issn2664-4487
dc.identifier.pages49-53
dc.identifier.urihttps://doi.org/10.2312/eurova.20201086
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20201086
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.titleCongnostics: Visual Features for Doubly Time Series Plotsen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
049-053.pdf
Size:
3.37 MB
Format:
Adobe Portable Document Format
Download
Collections
EuroVA2020