A hybrid CEEMDAN-VMD-TimesNet model for significant wave height prediction in the South Sea of China

Accurate prediction of significant wave height is of great reference value for wave energy generation. However, due to the non-linearity and non-stationarity of significant wave height, traditional algorithms face difficulties in achieving satisfactory prediction results. In this study, a hybrid CEEMDAN-VMD-TimesNet model is proposed for non-stationary significant wave height prediction. Based on the significant wave height in the South Sea of China, the performance of the SVM model, the GRU model, the LSTM model, the TimesNet model, the CEEMDAN-TimesNet model and the CEEMDAN-VMD-TimesNet model are compared in terms of multi-step prediction. It is found that the prediction accuracy of the TimesNet model is higher than that of the SVM model, the GRU model and the LSTM model. The non-stationarity of significant wave height is reduced by CEEMDAN decomposition. Thus, the CEEMDAN-TimesNet model performs better than the TimesNet model in predicting significant wave height. The prediction accuracy of the CEEMDAN-VMD-TimesNet model is further improved by employing VMD for the secondary decomposition of components with high and moderate complexity. Additionally, the CEEMDAN-VMD-TimesNet model can accurately predict trends and extreme values of significant wave height with minimal phase shifts even during typhoon periods. The results demonstrate that the CEEMDAN-VMD-TimesNet model exhibits superiority in predicting significant wave height.