Tag: spatio-temporal modeling

A new Spatio-Temporal Graph Transformer model predicts urban traffic speeds across multiple horizons by integrating fixed road network structures with ...

Researchers developed a unified spatio-temporal graph attention transformer framework that achieved 85.9 percent accuracy on DEAP and 91.3 percent accuracy ...

Researchers developed IBformer, an architecture that introduces inductive bias and specialized enhancement modules to tackle spatial and temporal heterogeneity across ...

A new dynamic graph neural framework called GAMMA-Traffic updates road-network connections in real time using cross-correlation analysis, reporting up to ...

A new skeleton-based framework, TaichiNet, combines local spatio-temporal graph modeling with a Transformer encoder to recognize Tai Chi movements, outperforming ...

A hybrid deep learning framework that combines ant colony optimization with a spatio-temporal variational autoencoder recognized nine opera teaching postures ...

A study in Delhi, Mysore and Surat found that clustering road segments before training deep-learning models improved bus arrival-time predictions ...

A dual-graph deep learning network that hard-codes petroleum physics alongside learned statistics cuts oil production prediction error to about 20 ...

A rigorous benchmark of six machine learning families across eight Ahmedabad monitoring stations finds that a hybrid GCN-LSTM model only ...

Researchers in India have developed CAT-Net, a hybrid deep learning model combining convolutional networks, coordinate attention, and Transformer encoders to ...