A New Hybrid Prediction Method of El Niño/La Niña Events by Combining TimesNet and ARIMA

El Niño/La Niña events significantly impact human society, often resulting in considerable monetary losses. Accurate prediction has become crucial with triple La Niña events in this century. This study applied TimesNet to El Niño/La Niña event prediction for the first time. We proposed a hybrid prediction method based on extracting features from time series data and initially decomposing the time series data (Niño3.4) into several Intrinsic Mode Functions (IMFs) using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Based on the characteristics of each IMF, we used a hybrid method of TimesNet and ARIMA to make adaptive forecasts for them. We selected monthly data from 1950 to 2022, with the first 63 years used for training and shifted 12 periods (12 months) ahead to forecast the Niño3.4 index values for the next ten years. The experimental results of this study show that: 1) The pre-processing method using CEEMDAN can effectively extract the original time series data features and significantly improve the prediction performance; 2) proposed approach achieved good performance in predicting El Niño/La Niña events, particularly during the transition from El Niño to La Niña events (e.g., 2016, 2019-2020); 3) evaluation results indicate that the proposed model exhibits better predictive power (stability and accuracy of prediction results) than the current best single-order predictor, the ConvLSTM model, on the validation set of the last ten years.