OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting

• URL: http://arxiv.org/abs/2601.06078v1
• Date: Mon, 29 Dec 2025 22:27:15 GMT
• Title: OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting
• Authors: Yin Wang, Chunlin Gong, Zhuozhen Xu, Lehan Zhang, Xiang Wu,
• Abstract summary: We propose OptFormer, a novel optical encoder-decoder model that integrates phase-space reconstruction with a motion-aware attention mechanism guided by flow.<n>Experiments on NOAA SST datasets across multiple spatial scales demonstrate that OptFormer achieves superior performance under a 1:1 training-to-prediction setting.
• Score: 4.206799880454911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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