2018
Banesh, Divya; Wendelberger, Joanne; Petersen, Mark; Ahrens, James; Hamann, Bernd
Change Point Detection for Ocean Eddy Analysis Proceedings Article
In: Proceedings of the Workshop on Visualisation in Environmental Sciences, pp. 27–33, Eurographics Association, Brno, Czech Republic, 2018, ISBN: 978-3-03868-063-5.
Abstract | Links | BibTeX | Tags: exploratory data analysis, image processing, object detection, regression analysis, time series analysis
@inproceedings{Banesh:2018:CPD:3310180.3310186,
title = {Change Point Detection for Ocean Eddy Analysis},
author = {Divya Banesh and Joanne Wendelberger and Mark Petersen and James Ahrens and Bernd Hamann},
url = {http://dl.acm.org/citation.cfm?id=3310180.3310186
https://dsscale.org/wp-content/uploads/2019/10/dbanesh_ChangeDetection_optimized.pdf},
isbn = {978-3-03868-063-5},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Workshop on Visualisation in Environmental Sciences},
pages = {27--33},
publisher = {Eurographics Association},
address = {Brno, Czech Republic},
series = {EnvirVis '18},
abstract = {The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.},
keywords = {exploratory data analysis, image processing, object detection, regression analysis, time series analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.
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1.
Banesh, Divya; Wendelberger, Joanne; Petersen, Mark; Ahrens, James; Hamann, Bernd
Change Point Detection for Ocean Eddy Analysis Proceedings Article
In: Proceedings of the Workshop on Visualisation in Environmental Sciences, pp. 27–33, Eurographics Association, Brno, Czech Republic, 2018, ISBN: 978-3-03868-063-5.
@inproceedings{Banesh:2018:CPD:3310180.3310186,
title = {Change Point Detection for Ocean Eddy Analysis},
author = {Divya Banesh and Joanne Wendelberger and Mark Petersen and James Ahrens and Bernd Hamann},
url = {http://dl.acm.org/citation.cfm?id=3310180.3310186
https://dsscale.org/wp-content/uploads/2019/10/dbanesh_ChangeDetection_optimized.pdf},
isbn = {978-3-03868-063-5},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Workshop on Visualisation in Environmental Sciences},
pages = {27--33},
publisher = {Eurographics Association},
address = {Brno, Czech Republic},
series = {EnvirVis '18},
abstract = {The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.