Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

Spatiotemporal forecasting tasks, such as traffic flow, combustion dynamics, and weather forecasting, often require complex models that suffer from low training efficiency and high memory consumption. This paper proposes a lightweight framework, Spectral Decoupled Knowledge Distillation, which transfers the multi-scale spatiotemporal representations from a complex teacher model to a more efficient lightweight student network. The teacher model follows an encoder-latent evolution-decoder architecture, where its latent evolution module decouples high-frequency details (e.g., instant traffic fluctuations) and low-frequency trends (e.g. long-term weather evolution) using convolution (local high-frequency extractor) and Transformer (global low-frequency modeler). However, the multi-layer convolution and deconvolution structures result in slow training and high memory usage. To address these issues, we propose a frequency-aligned knowledge distillation strategy, which extracts multi-scale spectral features from the teacher’s latent space, including high and low frequency components, to guide the lightweight student model (e.g., ResNet, U-Net) in capturing both local fine-grained variations and global evolution patterns. Experiments show that the student model achieves over 95% of the teacher’s forecasting accuracy while using only 20%-30% of its memory, with training speed improved by more than 50%. Our theoretical analysis reveals that the frequency-domain decoupling enables the student model to capture long-range dependencies without the need for complex structures. The frequency-aligned distillation mechanism further mitigates the inherent bias of lightweight models in cross-scale spatiotemporal dynamics modeling. This framework offers an effective and general solution for high-accuracy spatiotemporal forecasting in resource-constrained scenarios.