Deep Learning

GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding environment, such as the air quality. These sensors generate multiple geo-sensory time series, with spatial correlations …

HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

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 …