Meta Learning

Spatio-Temporal Meta Learning for Urban Traffic Prediction

Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging in three aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between …

Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

Predicting urban traffic (e.g., flow, speed) is of great importance to intelligent transportation systems and public safety, yet is very challenging as it is affected by two aspects: 1) complex spatio-temporal correlations of urban traffic, including …

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 …