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arXiv · 2005.02140

BlackBox: Generalizable Reconstruction of Extremal Values from Incomplete Spatio-Temporal Data

Tomislav Ivek·Domagoj Vlah
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Abstract

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.

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Tomislav Ivek, Domagoj Vlah. 2020-04-30. BlackBox: Generalizable Reconstruction of Extremal Values from Incomplete Spatio-Temporal Data. https://arxiv.org/abs/2005.02140

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