计算机应用与软件2023，Vol.40Issue(12)：119-125,146,8.DOI:10.3969/j.issn.1000-386x.2023.12.018
基于CEEMDAN与LSTM-Attention的股市预测模型
THE STOCK MARKET PREDICTION MODEL BASED ON CEEMDAN AND LSTM-ATTENTION
孙晨宇 1张树东1
作者信息
- 1. 首都师范大学信息工程学院 北京 100089
- 折叠
摘要
Abstract
Financial stock data with time-series characteristics are non-linear,non-stationary and complex dynamic,which poses a challenge to the prediction model.This paper proposes the LSTM-Attention model based on adaptive noise complete set of empirical mode decomposition.By restructuring after the high,medium and low frequency components,a more refined LSTM-Attention model was built.Target forecast was obtained by aggregation integration.The analysis of the experimental results shows that the mean absolute error(MAE),the root mean square error(RMSE),mean square error(MSE)and decision coefficient of this method are better than those of existing models,and it effectively improves the model prediction accuracy,and reduce the computational overhead.关键词
LSTM/经验模态分解/Seq2Seq模型/Attention机制/股票预测Key words
LSTM/Empirical mode decomposition/Seq2Seq model/Attention mechanism/Stock prediction分类
信息技术与安全科学引用本文复制引用
孙晨宇,张树东..基于CEEMDAN与LSTM-Attention的股市预测模型[J].计算机应用与软件,2023,40(12):119-125,146,8.
