Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data

2 Apr 2018Bao WangXiyang LuoFangbo ZhangBaichuan YuanAndrea L. BertozziP. Jeffrey Brantingham

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... (read more)

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