This paper introduces a novel residual correlation analysis, called AZ-analysis, to assess the optimality of spatio-temporal predictive models. The proposed AZ-analysis constitutes a valuable asset for discovering and highlighting those space-time regions where the model can be improved with respect to performance. The AZ-analysis operates under very mild assumptions and is based on a spatio-temporal graph that encodes serial and functional dependencies in the data; asymptotically distribution-free summary statistics identify existing residual correlation in space and time regions, hence localizing time frames and/or communities of sensors, where the predictor can be improved.

翻译：本文提出一种名为AZ-分析的新型残差相关性分析方法，用于评估时空预测模型的最优性。所提出的AZ-分析能够有效发现并凸显模型性能可提升的时空区域，为模型改进提供重要依据。该方法基于编码数据序列与功能依赖关系的时空图，在极弱假设条件下运行；其渐近分布无关的汇总统计量可识别特定时空区域存在的残差相关性，从而定位可提升预测器性能的时间窗口和/或传感器群组。