Sea Surface Temperature (SST) prediction plays a vital role in climate modeling and disaster forecasting. However, it remains challenging due to its nonlinear spatiotemporal dynamics and extended prediction horizons. To address this, we propose OptFormer, a novel encoder-decoder model that integrates phase-space reconstruction with a motion-aware attention mechanism guided by optical flow. Unlike conventional attention, our approach leverages inter-frame motion cues to highlight relative changes in the spatial field, allowing the model to focus on dynamic regions and capture long-range temporal dependencies more effectively. Experiments on NOAA SST datasets across multiple spatial scales demonstrate that OptFormer achieves superior performance under a 1:1 training-to-prediction setting, significantly outperforming existing baselines in accuracy and robustness.
@article{arxiv.2601.06078,
title = {OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting},
author = {Yin Wang and Chunlin Gong and Zhuozhen Xu and Lehan Zhang and Xiang Wu},
journal= {arXiv preprint arXiv:2601.06078},
year = {2026}
}