Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Ranked #3 on Traffic Prediction on PEMS-BAY
Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatiotemporal correlations, which vary from location to location and depend on the surrounding geographical information, e. g., points of interests and road networks.
The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs).
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #1 on Traffic Prediction on PeMS04
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temporally-varying dynamics that is realistic.
Active learning (AL) could contribute to solving critical environmental problems through improved spatio-temporal predictions.