标题：Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM

中国科学院文献情报中心期刊分区：计算机科学1区TOP（2022）

本校作者：梁晏慧；林宇*;

来源出版物：EXPERT SYSTEMS WITH APPLICATIONS  卷: 206  文献号: 117847 

出版年：2022

第一地址：成都理工大学

关键词：Gold price prediction；Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN)；Long Short-Term Memory (LSTM)；Convolutional Neural Networks (CNN)；Convolutional Block Attention Module (CBAM)；Model Confidence Set (MCS) test

备注： 本文已入围2023年11月/12月ESI高被引论文（TOP Papers）

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摘要：Gold price has always played an important role in the world economy and finance. In order to predict the gold price more accurately, this paper proposes a novel decomposition-ensemble model. Firstly, the original gold prices are decomposed into sublayers with different frequencies by the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). Secondly, the long short-term memory, convolutional neural networks, and convolutional block attention module (LSTM-CNN-CBAM) joint forecasting all sublayers. Finally, the prediction of the sublayers with different models is reconstructed as the final predicted results with the summation method. Among them, the proposed model could capture the essence of sequence effectively through ICEEMDAN algorithm, extract the long-term effect of the gold price by LSTM, mining the deep features of gold price data with CNN, and improving the feature extraction ability of the network through CBAM. It is proved by experiment that the cooperation among LSTM, CNN and CBAM can strengthen the modeling ability and improve the prediction accuracy. Moreover, the decomposition algorithm ICEEMDAN can further increase the forecast precision, and the prediction effect is better than other decomposition methods. Overall, the novel model ICEEMDAN-LSTM-CNN-CBAM (ILCC) could enhance the prediction accuracy and outperform other related comparative models.

文章链接地址: https://www.sciencedirect.com/science/article/pii/S0957417422011034?via%3Dihub