  We present a generic framework for spatio-temporal (ST) data modeling,
analysis, and forecasting, with a special focus on data that is sparse in both
space and time. Our multi-scaled framework is a seamless coupling of two major
components: a self-exciting point process that models the macroscale
statistical behaviors of the ST data and a graph structured recurrent neural
network (GSRNN) to discover the microscale patterns of the ST data on the
inferred graph. This novel deep neural network (DNN) incorporates the real time
interactions of the graph nodes to enable more accurate real time forecasting.
The effectiveness of our method is demonstrated on both crime and traffic
forecasting.
