用相空间重构提升海温预测精度，数据少也能准。
STFM: A Spatio-Temporal Information Fusion Model Based on Phase Space Reconstruction for Sea Surface Temperature Prediction
- 通过相空间重构构建延迟吸引子对，捕捉海温动态
- 仅需少量数据训练，预测误差显著低于传统方法
- 适合数据稀缺但需高精度的海洋环境预测场景
海表温度（SST）是关键环境参数，对生产规划优化至关重要，其准确预测是重要研究方向。然而，海洋动力系统的固有非线性带来了巨大挑战。现有方法主要包括基于物理的数值模拟和数据驱动的机器学习。前者虽通过微分方程描述SST演变，但计算复杂度高、适用性有限；后者虽计算高效，却依赖大量数据且可解释性差。本文提出一种纯数据驱动的预测框架，利用相空间重构构建具有数学同胚关系的初始-延迟吸引子对，并设计时空融合映射（STFM）以揭示其内在关联。与传统模型不同，该方法通过相空间重构高效捕捉SST动态，在对比实验中仅用少量训练数据即实现高预测精度。
原文摘要 · Abstract (English)
The sea surface temperature (SST), a key environmental parameter, is crucial to optimizing production planning, making its accurate prediction a vital research topic. However, the inherent nonlinearity of the marine dynamic system presents significant challenges. Current forecasting methods mainly include physics-based numerical simulations and data-driven machine learning approaches. The former, while describing SST evolution through differential equations, suffers from high computational complexity and limited applicability, whereas the latter, despite its computational benefits, requires large datasets and faces interpretability challenges. This study presents a prediction framework based solely on data-driven techniques. Using phase space reconstruction, we construct initial-delay attractor pairs with a mathematical homeomorphism and design a Spatio-Temporal Fusion Mapping (STFM) to uncover their intrinsic connections. Unlike conventional models, our method captures SST dynamics efficiently through phase space reconstruction and achieves high prediction accuracy with minimal training data in comparative tests
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