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.

我们描述了我们向极值分析2019数据挑战赛提交的材料，其中要求团队预测缺失数据的时空区域内海表温度异常的极端情况。我们提出了一个计算框架，该框架使用卷积深度神经网络重建缺失的数据。在不完整数据的条件下，我们采用类似于自动编码器的模型作为多元条件分布，使用推算噪声从中抽取完整数据集的可能重构。为了减轻由任何一个特定模型引入的偏差，构造了一个预测集合以创建极值的最终分布。我们的方法不依赖专家知识就能以最小的假设准确地再现复杂海洋系统的动态特征。