Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
نویسندگان
چکیده
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 paerns of the ST data on the inferred graph. is novel deep neural network (DNN) incorporates the real time interactions of the graph nodes to enable more accurate real time forecasting. e eectiveness of our method is demonstrated on both crime and trac forecasting.
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تاریخ انتشار 2018