融合通道与多头注意力的股价趋势预测模型  

Stock Price Trend Prediction Model Integrating Channel and Multi-Head Attention Mechanisms

作　　者：周佳妮 刘春雨 刘家鹏 ZHOU Jiani;LIU Chunyu;LIU Jiapeng(School of Economics and Management,China Jiliang University,Hangzhou 310018,China;School of Business,Zhejiang Wanli University,Ningbo,Zhejiang 315100,China)

机构地区：[1]中国计量大学经济与管理学院,杭州310018 [2]浙江万里学院商学院,浙江宁波315100

出　　处：《计算机工程与应用》2025年第8期324-338,共15页Computer Engineering and Applications

基　　金：国家社会科学基金(18BGL224)。

摘　　要：目前的传统模型如支持向量机(SVM)、循环神经网络(RNN)、长短期记忆网络(LSTM)等,在处理非线性、多尺度、高噪声的股票时间序列数据方面存在局限,往往无法有效提升股价趋势预测的准确性。针对这一问题,创新性地提出了一种基于通道注意力和多头注意力的深度学习预测模型(SDAE-CNN-BiLSTM-CM)。该模型融合了降噪自编码器和CNN-BiLSTM模型,能够对高噪声的股票数据有效建模,同时引入了通道注意力机制(CAM)和多头注意力机制(MSA),以更好地捕获时间序列的短期和长期依赖关系,最后通过联合优化层实现分层聚合时序信息,以适应金融时间序列时变性强的特点。实证结果表明,相较于传统模型,所提出的模型在提高股价趋势预测准确性上具有优势,且基于该模型的交易策略在回测表现中也获得了较高的收益与较低的风险。Current conventional models,such as support vector machine(SVM),recurrent neural network(RNN),and long short-term memory(LSTM),have limitations in handling nonlinear,multi-scale,and highly noisy stock time-series data,often failing to effectively improve the accuracy of stock price trend prediction.To address this issue,an innovative deep learning prediction model named SDAE-CNN-BiLSTM-CM,based on channel attention mechanism and multi-head self-attention mechanism,is proposed.This model integrates a stacked denoising auto-encoder(SDAE)and a CNNBiLSTM model to effectively model noisy stock data,and also introduces a channel attention mechanism(CAM)and a multi-head self-attention mechanism(MSA)to better capture the short-term and long-term dependencies of the time series.Finally,a joint optimization layer is utilized to achieve hierarchical aggregation of temporal information,adapting to the highly volatile nature of financial time series.Empirical results show that,compared with traditional models,the proposed model has significant advantages in improving prediction accuracy,and the trading strategy based on this model has also achieved higher returns and lower risks in backtesting performance.

关 键 词：股价趋势预测 深度学习 注意力机制 双向长短期记忆网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]