Print Email Facebook Twitter Spatio-temporal prediction of missing temperature with stochastic Poisson equations Title Spatio-temporal prediction of missing temperature with stochastic Poisson equations: The LC2019 team winning entry for the EVA 2019 data competition Author Cheng, D. (TU Delft Applied Probability) Liu, Z. (TU Delft Materials and Manufacturing) Date 2020 Abstract This paper presents our winning entry for the EVA 2019 data competition, the aim of which is to predict Red Sea surface temperature extremes over space and time. To achieve this, we used a stochastic partial differential equation (Poisson equation) based method, improved through a regularization to penalize large magnitudes of solutions. This approach is shown to be successful according to the competition’s evaluation criterion, i.e. a threshold-weighted continuous ranked probability score. Our stochastic Poisson equation and its boundary conditions resolve the data’s non-stationarity naturally and effectively. Meanwhile, our numerical method is computationally efficient at dealing with the data’s high dimensionality, without any parameter estimation. It demonstrates the usefulness of stochastic differential equations on spatio-temporal predictions, including the extremes of the process. Subject 35Q6262H1162M3062P12Data competitionEVA 2019 ConferencePoisson equationPredictionSpatio-temporal dataTemperature data To reference this document use: http://resolver.tudelft.nl/uuid:d1d17f9a-58f7-402d-af76-191fe78e3c06 DOI https://doi.org/10.1007/s10687-020-00397-w ISSN 1386-1999 Source Extremes: statistical theory and applications in science, engineering and economics, 24 (1), 163-175 Part of collection Institutional Repository Document type journal article Rights © 2020 D. Cheng, Z. Liu Files PDF Cheng_Liu2020_Article_Spa ... nOfMis.pdf 1.23 MB Close viewer /islandora/object/uuid:d1d17f9a-58f7-402d-af76-191fe78e3c06/datastream/OBJ/view