现代信息科技2024，Vol.8Issue(12)：155-159,163,6.DOI:10.19850/j.cnki.2096-4706.2024.12.033
基于Attention机制的CNN-LSTM概率预测模型的股指预测
Stock Index Prediction Based on CNN-LSTM Probability Prediction Model with Attention Mechanism
高欣1
作者信息
- 1. 安徽大学 大数据与统计学院,安徽 合肥 230601
- 折叠
摘要
Abstract
Given the high volatility of the securities market and the high difficulty of predicting it,this paper integrates the Attention Mechanism into the CNN-LSTM model based on the encoder-decoder structure.The Attention Mechanism is used to capture data dependency patterns between different time points,long series information is extracted,and based on this,a probability density function is provided for sampling prediction,point prediction and interval prediction of stock prices are obtained ultimately.The experimental results show that the CNN-LSTM probability prediction model incorporating the Attention Mechanism outperforms other benchmark models in terms of comprehensive performance,and can make high-precision multi-step predictions of the closing price of the Shanghai Composite Index.关键词
Attention机制/概率密度函数/上证指数Key words
Attention Mechanism/probability density function/Shanghai Composite Index分类
信息技术与安全科学引用本文复制引用
高欣..基于Attention机制的CNN-LSTM概率预测模型的股指预测[J].现代信息科技,2024,8(12):155-159,163,6.
