Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes
KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2019-12-02
Embargo End Date2021-01-16
Permanent link to this recordhttp://hdl.handle.net/10754/660734
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AbstractLarge, non-stationary spatio-temporal data are ubiquitous in modern statistical applications, and the modeling of spatio-temporal extremes is crucial for assessing risks in environmental sciences among others. While the modeling of extremes is challenging in itself, the prediction of rare events at unobserved spatial locations and time points is even more difficult. In this Editorial, we describe the data competition that was organized for the 11th international conference on Extreme-Value Analysis (EVA 2019), for which several teams modeled and predicted Red Sea surface temperature extremes over space and time. After introducing the dataset and the goal of the competition, we disclose the final ranking of the teams, and we finally discuss some interesting outcomes and future challenges.
CitationHuser, R. (2020). Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes. Extremes. doi:10.1007/s10687-019-00369-9
SponsorsI would like to thank Bojan Basrak, Hrvoje Planinic and the whole EVA 2019 conference local and scientific committees, for organizing such a successful conference. I also thank Olivier Wintenberger, Alec Stephenson, Holger Rootzen and Thomas Mikosch for their support, as well as for helpful discussions and advice on the data competition, and for providing feedback on an early draft of this Editorial. Finally, I thank and congratulate all teams, without whose active and positive participation this competition would not have taken place.
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3434.