On the Limits of Univariate Deep Learning for Significant Wave Height Forecasting
Read the original on arXiv Machine Learning →The paper performs a systematic hyperparameter search across five deep learning architectures (DLinear, LSTM, PatchTST, ResAttLstm, and Mamba2) and nine context lengths for forecasting significant wave height (Hs) at a single buoy, then re‑evaluates the best configurations on a 47‑buoy, 37‑year dataset. Across all models, performance converges to a very small spread (SD = 0.0014 m², 0.8% of the grand mean), yet all models still outperform persistence, though none consistently outperforms the others. The study finds that persistence already captures the dominant linear‑inertial signal in univariate Hs, and that model‑class differences are dwarfed by cross‑buoy variance, suggesting diminishing returns for further architecture engineering under univariate input settings.
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