南京信息工程大学学报2023，Vol.15Issue(6)：643-651,9.DOI:10.13878/j.cnki.jnuist.20221031001
基于BiLSTM-SA-TCN时间序列模型在股票预测中的应用
Application of BiLSTM-SA-TCN time series model in stock price prediction
摘要
Abstract
To address the poor timeliness and simple prediction functions of stock forecasting models,we propose a model abbreviated as BiLSTM-SA-TCN,which combines Bi-directional Long Short-Term Memory(Bi-LSTM)neural network,Self-Attention(SA)and Temporal Convolution Network(TCN).The learning unit and prediction unit in the proposed model can effectively learn important stock data,capture long-term dependency information,and output the predicted next day close price.The experimental results indicate that the BiLSTM-SA-TCN model has more stable prediction results on multiple data sets and has higher modle generalization ability.Furthormore,incomparative experiment,the BiLSTM-SA-TCN model achieves the lowest root mean square error,the lowest mean absolute error,and the best fitting degree of R2 on the majority of datasets.关键词
股票价格预测/长短期记忆网络/注意力机制/时间卷积网络Key words
stock price forecast/long short-term memory(LSTM)/attention mechanism/temporal convolution net-work(TCN)分类
信息技术与安全科学引用本文复制引用
杨智勇,叶玉玺,周瑜..基于BiLSTM-SA-TCN时间序列模型在股票预测中的应用[J].南京信息工程大学学报,2023,15(6):643-651,9.基金项目
重庆市自然科学基金(cstc2021ycjh-bgzxm0088) （cstc2021ycjh-bgzxm0088）
重庆市教育委员会科学技术研究计划项目(KJQN201903402) （KJQN201903402）
