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Found 5 papers, 3 papers with code
AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions.
Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
It first encodes traffic data into two disentangled representations for associating invariant and variant ST contexts.
Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning
The framework consists of two main components: an automatic graph decomposition module that decomposes the original graph structure inherent in ST data into subgraphs corresponding to different factors, and a decomposed learning network that learns the partial ST data on each subgraph separately and integrates them for the final prediction.
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods.
Ranked #1 on
Traffic Prediction
on BJTaxi
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities.
Physics-informed machine learning
Spatio-Temporal Forecasting
+1
