Congnostics: Visual features for doubly time series plots

Bao Nguyen, Rattikorn Hewett, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationEuroVA 2020 - EuroVis Workshop on Visual Analytics
PublisherEurographics Association
Pages49-53
Number of pages5
ISBN (Electronic)9783038681168
DOIs
StatePublished - 2020
Event11th International EuroVis Workshop on Visual Analytics, EuroVA 2020 at Eurographics/EuroVis 2020 - Virtual, Online, Sweden
Duration: May 25 2020 → …

Publication series

NameInternational Workshop on Visual Analytics
ISSN (Electronic)2664-4487

Conference

Conference11th International EuroVis Workshop on Visual Analytics, EuroVA 2020 at Eurographics/EuroVis 2020
Country/TerritorySweden
CityVirtual, Online
Period05/25/20 → …

Cite this