Significant Wave Height Forecasting Method for the North Atlantic Ocean Based on the CEEMDAN-iTransformer Model

Accurate forecasting of significant wave height (WVHT) is essential for marine disaster prevention, offshore operations, and coastal management. However, WVHT time series typically exhibit strong nonlinearity and non-stationarity, which pose significant challenges for reliable prediction, especially under complex sea conditions. To address these issues, a hybrid forecasting framework based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the iTransformer model is proposed for the North Atlantic Ocean. In the proposed method, the original WVHT time series is first decomposed into multiple intrinsic mode functions using CEEMDAN to alleviate non-stationarity and reveal multi-scale characteristics. Subsequently, the iTransformer model is employed to capture the temporal dependencies of each decomposed component, and the final prediction is obtained through reconstruction. Experiments are conducted using multi-variable buoy observations from the North Atlantic, incorporating meteorological and oceanographic factors. Results demonstrate that the proposed CEEMDAN-iTransformer model significantly improves forecasting accuracy and stability compared with baseline models across multiple prediction horizons. The framework shows strong capability in handling complex wave dynamics and provides an effective solution for high-precision WVHT forecasting.