用大模型提升海浪高度预测，更快更准还支持零样本推理。
Improving Significant Wave Height Prediction Using Chronos Models
- 基于大语言模型设计时间架构，识别历史波浪数据模式。
- 训练快14.3%，推断速度提升2.5倍，误差仅0.575 MASE。
- 短时与长时预测均领先，零样本下仍保持前四名表现。
精准的波高预测对海上安全和海岸韧性至关重要，但传统物理模型与机器学习方法在计算效率和非线性动态建模方面存在挑战。本文提出Chronos，首个基于大语言模型（LLM）的时间架构，专为波浪预报优化。通过在西北太平洋三个战略海域的历史波浪数据上应用先进的时间模式识别，该框架实现多模态改进：(1) 训练时间减少14.3%，推理速度提升2.5倍，达到0.575均方根缩放误差（MASE）；(2) 在1-24小时短时预报中全面领先；(3) 在1-120小时长期预报中持续保持优势；(4) 零样本能力表现优异，性能排名第四（12个模型中），优于专用业务模型。该基于大模型的时间建模范式为波浪预测树立新标准，提供高效解决方案，并可推广至复杂地球物理系统建模。
原文摘要 · Abstract (English)
Accurate wave height prediction is critical for maritime safety and coastal resilience, yet conventional physics-based models and traditional machine learning methods face challenges in computational efficiency and nonlinear dynamics modeling. This study introduces Chronos, the first implementation of a large language model (LLM)-powered temporal architecture (Chronos) optimized for wave forecasting. Through advanced temporal pattern recognition applied to historical wave data from three strategically chosen marine zones in the Northwest Pacific basin, our framework achieves multimodal improvements: (1) 14.3% reduction in training time with 2.5x faster inference speed compared to PatchTST baselines, achieving 0.575 mean absolute scaled error (MASE) units; (2) superior short-term forecasting (1-24h) across comprehensive metrics; (3) sustained predictive leadership in extended-range forecasts (1-120h); and (4) demonstrated zero-shot capability maintaining median performance (rank 4/12) against specialized operational models. This LLM-enhanced temporal modeling paradigm establishes a new standard in wave prediction, offering both computationally efficient solutions and a transferable framework for complex geophysical systems modeling.
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