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

• URL: http://arxiv.org/abs/2005.02140v3
• Date: Thu, 8 Oct 2020 16:36:22 GMT
• Title: BlackBox: Generalizable Reconstruction of Extremal Values from Incomplete Spatio-Temporal Data
• Authors: Tomislav Ivek, Domagoj Vlah
• Abstract summary: We present a framework to reconstruct missing data using convolutional deep neural networks. In order to mitigate bias introduced by any one particular model, a prediction ensemble is constructed. Our method does not rely on expert knowledge in order to accurately reproduce dynamic features of a complex oceanographic system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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