In this study, we present a hypergraph convolutional recurrent neural network (HGC-RNN), which is a prediction model for structured,time-series sensor network data. Representing sensor networks in,a graph structure is useful for expressing structural relationships,among sensors. Conventional graph structure, however, has a limitation on representing complex structure in real world application,,such as shared connections among multiple nodes. We use a hypergraph, which is capable of modeling complicated structures, for,structural representation. HGC-RNN performs a hypergraph convolution operation on the input data represented in the hypergraph to,extract hidden representations of the input, while considering the,structural dependency of the data. HGC-RNN employs a recurrent,neural network structure to learn temporal dependency from the,data sequence. We conduct experiments to forecast taxi demand,in NYC, traffic flow in the overhead hoist transfer system, and gas,pressure in a gas regulator. We compare the performance of our,method with those of other existing methods, and the result shows,that HGC-RNN has strengths over baseline models.
