HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

Understand

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.