计算机工程与应用2024，Vol.60Issue(17)：272-281,10.DOI:10.3778/j.issn.1002-8331.2312-0329
RF-MIP-LSTM股价预测模型
RF-MIP-LSTM Stock Price Prediction Model
摘要
Abstract
Long short-term memory(LSTM)neural networks have demonstrated superior performance in predicting com-plex nonlinear systems such as stock price fluctuations.However,the traditional LSTM models do not take into account the coupling relationships among the three gating mechanisms and the impact of long-term memory on the model's input.This paper enhances the transmission of long-term memory peeking information and the stability of the model by incorpo-rating long-term memory into the input gate and coupling the three gating mechanisms into a unique gate mechanism.It constructs an LSTM model based on feature selection with random forest(RF-MIP-LSTM)and derives the forward and backward propagation algorithms for the model.Through predictions and comparisons on stock prices of Agricultural Bank of China,Yantian Port,Gree Electric Appliances,and the Shanghai Stock Exchange Index,the RF-MIP-LSTM model exhibits superior convergence speed and predictive accuracy compared to the traditional LSTM model.关键词
股价预测/随机森林(RF)/长短时记忆(LSTM)神经网络/长时窥视孔Key words
stock price prediction/random forest(RF)/long short-term memory(LSTM)neural network/long peephole分类
信息技术与安全科学引用本文复制引用
张颖,李路..RF-MIP-LSTM股价预测模型[J].计算机工程与应用,2024,60(17):272-281,10.基金项目
国家自然科学基金(62173222). （62173222）
