El Nino is an important quasi-cyclical climate phenomenon that can have a significant impact on ecosystems and societies. Due to the chaotic nature of the atmosphere and ocean systems, traditional methods (such as statistical methods) are difficult to provide accurate El Nino index predictions. The latest research shows that Ensemble Empirical Mode Decomposition (EEMD) is suitable for analyzing non-linear and non-stationary signal sequences, Convolutional Neural Network (CNN) is good at local feature extraction, and Recurrent Neural Network (RNN) can capture the overall information of the sequence. As a special RNN, Long Short-Term Memory (LSTM) has significant advantages in processing and predicting long, complex time series. In this paper, to predict the El Nino index more accurately, we propose a new hybrid neural network model, EEMD-CNN-LSTM, which combines EEMD, CNN, and LSTM. In this hybrid model, the original El Nino index sequence is first decomposed into several Intrinsic Mode Functions (IMFs) using the EEMD method. Next, we filter the IMFs by setting a threshold, and we use the filtered IMFs to reconstruct the new El Nino data. The reconstructed time series then serves as input data for CNN and LSTM. The above data preprocessing method, which first decomposes the time series and then reconstructs the time series, uses the idea of symmetry. With this symmetric operation, we extract valid information about the time series and then make predictions based on the reconstructed time series. To evaluate the performance of the EEMD-CNN-LSTM model, the proposed model is compared with four methods including the traditional statistical model, machine learning model, and other deep neural network models. The experimental results show that the prediction results of EEMD-CNN-LSTM are not only more accurate but also more stable and reliable than the general neural network model.

中文翻译：

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使用带集成经验模式分解的深度学习进行厄尔尼诺指数预测

厄尔尼诺现象是一种重要的准周期性气候现象，会对生态系统和社会产生重大影响。由于大气和海洋系统的混沌特性，传统方法（如统计方法）难以提供准确的厄尔尼诺指数预测。最新研究表明，Ensemble Empirical Mode Decomposition (EEMD) 适用于分析非线性和非平稳信号序列，Convolutional Neural Network (CNN) 擅长局部特征提取，Recurrent Neural Network (RNN) 可以捕捉整体序列的信息。作为一种特殊的 RNN，长短期记忆 (LSTM) 在处理和预测长而复杂的时间序列方面具有显着优势。在本文中，为了更准确地预测厄尔尼诺指数，我们提出了一种新的混合神经网络模型，EEMD-CNN-LSTM，它结合了 EEMD、CNN 和 LSTM。在这个混合模型中，原始的厄尔尼诺指数序列首先使用 EEMD 方法分解为几个内在模式函数 (IMF)。接下来，我们通过设置阈值来过滤 IMF，我们使用过滤后的 IMF 来重建新的厄尔尼诺数据。重建的时间序列然后作为 CNN 和 LSTM 的输入数据。上述数据预处理方法，先对时间序列进行分解，再对时间序列进行重构，利用了对称性的思想。通过这种对称操作，我们提取有关时间序列的有效信息，然后根据重建的时间序列进行预测。为了评估 EEMD-CNN-LSTM 模型的性能，将所提出的模型与四种方法进行比较，包括传统统计模型、机器学习模型、和其他深度神经网络模型。实验结果表明，与一般神经网络模型相比，EEMD-CNN-LSTM的预测结果不仅更加准确，而且更加稳定可靠。