dc.contributor.author | Nguyen, Bao Dien Quoc | en_US |
dc.contributor.author | Hewett, Rattikorn | en_US |
dc.contributor.author | Dang, Tommy | en_US |
dc.contributor.editor | Turkay, Cagatay and Vrotsou, Katerina | en_US |
dc.date.accessioned | 2020-05-24T13:31:31Z | |
dc.date.available | 2020-05-24T13:31:31Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-3-03868-116-8 | |
dc.identifier.issn | 2664-4487 | |
dc.identifier.uri | https://doi.org/10.2312/eurova.20201086 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurova20201086 | |
dc.description.abstract | In 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.publisher | The Eurographics Association | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | ] |
dc.title | Congnostics: Visual Features for Doubly Time Series Plots | en_US |
dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | |
dc.description.sectionheaders | Visual Analysis of High Dimensional and Temporal Data | |
dc.identifier.doi | 10.2312/eurova.20201086 | |
dc.identifier.pages | 49-53 | |