论文标题
BlackBox：从不完整时空数据中对极值的可推广重建
BlackBox: Generalizable Reconstruction of Extremal Values from Incomplete Spatio-Temporal Data
论文作者
论文摘要
我们描述了我们对极端价值分析2019数据挑战的提交，其中要求团队在缺失数据的时空区域内预测海面温度异常的极端。我们提出了一个计算框架，该框架使用卷积深神经网络重建丢失的数据。以不完整的数据为条件，我们采用类似自动编码器的模型作为多元条件分布，从中使用估算的噪声对完整数据集进行了可能的重建。为了减轻任何一个特定模型引入的偏差，构建了一个预测集合以创建最终的极值分布。我们的方法不依赖专家知识来准确地重现具有最小假设的复杂海洋学系统的动态特征。获得的结果有望可重复使用和对其他领域的概括。
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.
