  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.
