Naslov
BlackBox: Generalizable reconstruction of extremal values from incomplete spatio-temporal data

Autori
Ivek, Tomislav ; Vlah, Domagoj

Izvornik
Extremes (1386-1999) 24 (2021); 145-162

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Convolutional neural network ; Data reconstruction ; Deep learning ; Extreme Value Analysis Conference challenge ; Ensemble ; Spatio-temporal extremes

Sažetak
We describe our submission to the Extreme Value Analysis 2019 Data Challenge in which teams were asked to predict extremes of sea surface temperature anomaly within spatio-temporal regions of missing data. We present a computational framework which reconstructs missing data using convolutional deep neural networks. Conditioned on incomplete data, we employ autoencoder-like models as multivariate conditional distributions from which possible reconstructions of the complete dataset are sampled using imputed noise. In order to mitigate bias introduced by any one particular model, a prediction ensemble is constructed to create the final distribution of extremal values. Our method does not rely on expert knowledge in order to accurately reproduce dynamic features of a complex oceanographic system with minimal assumptions. The obtained results promise reusability and generalization to other domains.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Geofizika, Računarstvo

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