为精细的时空预测建立自相关感知表征

许多科学预测问题都有与时空数据和建模相关的挑战，即只使用稀疏和不均匀分布的观测数据来处理空间和时间的复杂变化。本文提出了一个新颖的深度学习架构，即依赖位置的时间-Eries数据的深度学习预测（DeepLATTE），明确地将空间统计学理论纳入神经网络以解决这些挑战。除了特征选择模块和时空学习模块，DeepLATTE还包含一个自相关引导的半监督学习策略，以强制要求所学时空嵌入空间中的预测的局部自相关模式和全局自相关趋势与观测数据一致，克服了观测数据稀疏和分布不均的限制。在训练过程中，监督和半监督损失都指导整个网络的更新，以。1）防止过度拟合，2）完善特征选择，3）学习有用的时空表征，以及4）改善整体预测。我们使用公开的数据对DeepLATTE进行了演示，该数据适用于一个重要的公共卫生话题，即空气质量预测，并在一个被充分研究的复杂物理环境中--洛杉矶。实验表明，所提出的方法提供了准确的精细空间尺度的空气质量预测，并揭示了影响结果的关键环境因素。

原文题目：Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction

原文题目：Many scientific prediction problems have spatiotemporal data- and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distributed observations. This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates theories of spatial statistics into neural networks to address these challenges. In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations. During the training process, both supervised and semi-supervised losses guide the updates of the entire network to: 1) prevent overfitting, 2) refine feature selection, 3) learn useful spatiotemporal representations, and 4) improve overall prediction. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction, in a well-studied, complex physical environment - Los Angeles. The experiment demonstrates that the proposed approach provides accurate fine-spatial-scale air quality predictions and reveals the critical environmental factors affecting the results.