Euler Frequency Modulation for Non-Stationary Spatiotemporal Graph Traffic Forecasting

Short-term traffic forecasting is essential for urban transportation management. While traffic flow exhibits regular day-to-day periodic patterns, spatiotemporal fluctuations make accurate prediction challenging. Conventional traffic flow prediction models have difficulty capturing these variations, particularly when spatiotemporal dependencies become highly non-stationary. To address this issue, this study proposes Euler Frequency Spatiotemporal Graph Neural Network(EFSG), a spatiotemporal model that uses Euler Frequency Modulation (EFM), which applies FFT on each short input window and jointly edits amplitude and phase for every sample, node, and channel. EFSG further builds a spectrum-aware dynamic graph linking nodes with similar spectral patterns beyond physical adjacency, and a gated fusion layer adaptively combines local and frequency-domain features. Ablation studies reveal that EFM and spectral propagation function as a coupled unit driving performance gains. Experiments on four real-world datasets show that EFSG achieves a 19.9% reduction in MAE on NYC-Bike pick-up and a 60% reduction in GPU memory consumption on PEMS04 compared with TEDDCF, while converging within 20 epochs.