Er Zhou

Comparative Analysis of LSTM and PatchTST Models for Urban Street Traffic Time Series Forecasting

Although the forecasting accuracy of both models decreases with longer prediction horizons, PatchTST maintains relatively lower errors and better stability, and the results indicate that the patch-based representation and attention mechanism provide advantages in capturing both local variations and long-term temporal dependencies in urban traffic sequences.