Empirical comparison of LSTM, GNN, and Transformer architectures for NBA trajectory forecasting finds hybrid LSTM with contextual information yields lowest FDE of 1.51m over horizons up to 2s.
Rethinking Trajectory Prediction via Team Game,
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Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
Empirical comparison of LSTM, GNN, and Transformer architectures for NBA trajectory forecasting finds hybrid LSTM with contextual information yields lowest FDE of 1.51m over horizons up to 2s.