Congnostics: Visual Features for Doubly Time Series Plots

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049-053.pdf (3.37 MB)
Date
2020
Authors
Nguyen, Bao Dien Quoc
Hewett, Rattikorn
Dang, Tommy
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description

        
@inproceedings{
10.2312:eurova.20201086
,
booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)
},
editor = {
Turkay, Cagatay and Vrotsou, Katerina
},
title = {{
Congnostics: Visual Features for Doubly Time Series Plots
}},
author = {
Nguyen, Bao Dien Quoc
 and 
Hewett, Rattikorn
 and 
Dang, Tommy
},
year = {
2020
},
publisher = {
The Eurographics Association
},
ISSN = {
2664-4487
},
ISBN = {
978-3-03868-116-8
},
DOI = {
10.2312/eurova.20201086
}
}
Citation
URI
https://doi.org/10.2312/eurova.20201086
 https://diglib.eg.org:443/handle/10.2312/eurova20201086
Collections
EuroVA2020