计算机工程与应用2024，Vol.60Issue(15)：284-296,13.DOI:10.3778/j.issn.1002-8331.2304-0303
基于MVC5B混合模型的中国股指预测研究
Research on Chinese Stock Index Prediction Based on MVC5B Hybrid Model
崔晨豪 1李勇1
作者信息
- 1. 中国科学技术大学 管理学院,合肥 230026
- 折叠
摘要
Abstract
In order to improve the predictive performance of Chinese stock indices,a hybrid model MVC5B(multi-channel-VMD-CBAM5-BiLSTM)is proposed,which integrates variational mode decomposition(VMD),convolutional block attention module(CBAM),and bidirectional long short-term memory network(BiLSTM).Unlike the commonly used decomposition-ensemble construction approach in hybrid models,MVC5B is constructed based on a proposed multi-channel input method.The multi-channel input method effectively avoids the cumulative errors and significant computational costs associated with the multiple predictions of decomposition-ensemble approach,thereby enhancing the predictive per-formance of MVC5B.Furthermore,the introduction of CBAM not only improves the predictive performance of the stock indices but also enriches the research on CBAM in stock index prediction.Empirical results based on multiple representative Chinese stock index datasets demonstrate that MVC5B exhibits significantly better predictive performance and simulated returns than popular forecasting models.Additionally,the empirical results further confirm the superiority of the multi-channel input method over the decomposition-ensemble approach and the effectiveness of CBAM in stock index prediction.关键词
股指预测/卷积注意力模块/双向长短期记忆网络/变分模态分解/多通道输入Key words
stock index prediction/convolutional block attention module(CBAM)/bidirectional long short-term memory network(BiLSTM)/variational mode decomposition(VMD)/multi-channel input method分类
信息技术与安全科学引用本文复制引用
崔晨豪,李勇..基于MVC5B混合模型的中国股指预测研究[J].计算机工程与应用,2024,60(15):284-296,13.
