  Spatio-temporal (ST) data, which represent multiple time series data
corresponding to different spatial locations, are ubiquitous in real-world
dynamic systems, such as air quality readings. Forecasting over ST data is of
great importance but challenging as it is affected by many complex factors,
including spatial characteristics, temporal characteristics and the intrinsic
causality between them. In this paper, we propose a general framework
(HyperST-Net) based on hypernetworks for deep ST models. More specifically, it
consists of three major modules: a spatial module, a temporal module and a
deduction module. Among them, the deduction module derives the parameter
weights of the temporal module from the spatial characteristics, which are
extracted by the spatial module. Then, we design a general form of HyperST
layer as well as different forms for several basic layers in neural networks,
including the dense layer (HyperST-Dense) and the convolutional layer
(HyperST-Conv). Experiments on three types of real-world tasks demonstrate that
the predictive models integrated with our framework achieve significant
improvements, and outperform the state-of-the-art baselines as well.
