BlackBox: Generalizable Reconstruction of Extremal Values from Incomplete Spatio-Temporal Data
This work addresses the challenge of reconstructing extreme values from incomplete data for oceanographic and related domains, but it appears incremental as it builds on existing deep learning techniques.
The authors tackled the problem of predicting extreme sea surface temperature anomalies in regions with missing spatio-temporal data by developing a computational framework using convolutional deep neural networks and ensemble methods, achieving results that promise reusability and generalization to other domains.
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.