Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
This work addresses forecasting challenges for sparse spatio-temporal data in domains like crime and traffic, representing an incremental improvement through hybrid methods.
The authors tackled the problem of modeling and forecasting sparse spatio-temporal data by developing a framework combining a self-exciting point process for macroscale statistics and a graph-structured recurrent neural network for microscale patterns, achieving more accurate real-time forecasting as demonstrated on crime and traffic datasets.
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.