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); E (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.

Questo articolo esplora i giri e le loro implicazioni.

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