Deep Learning for Spatio-Temporal Data
Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data. Deep neural networks (DNNs) have been successfully applied to various problems, such as computer vision, speech recognition, natural language understanding. Being different from these domains, ST data has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). It is very challenging to capture all of these ST properties simultaneously.
- Tutorial on Deep Learning for Spatio-Temporal Data in Urban Computing Summer School 2020
- AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph
- Fine-grained Urban Flow Prediction
- Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
- Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
- AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction