基于TRSSA-ELM算法的股价预测研究  

Research on Stock Price Prediction Based on TRSSA-ELM Algorithm

作　　者：谭佳伟 谷佳澄 李春梅 王善求 秦丹丹[2] TAN Jiawei;GU Jiacheng;LI Chunmei;WANG Shanqiu;QIN Dandan(School of Mathematics and Statistics,Changchun University of Technology,Changchun 130012,China;Fundamental Department,Aviation University of Air Force,Changchun 130012,China)

机构地区：[1]长春工业大学数学与统计学院,长春130012 [2]空军航空大学基础部,长春130012

出　　处：《吉林大学学报(信息科学版)》2025年第1期90-97,共8页Journal of Jilin University(Information Science Edition)

基　　金：吉林省教育厅基金资助项目(JJKH20220663KJ)。

摘　　要：针对股价预测中存在的不确定性、间断性、随机性和非线性等问题,提出一种TRSSA-ELM(Tent Random Walk Sparrow Optimization Algorithm-Extreme Learning Machine)股价预测模型。首先,采用自适应Tent混沌映射和随机游走策略对算法进行改进,增强种群多样性和随机性,提高算法局部和全局的寻优能力。其次,使用单峰、多峰和固定维多峰测试函数对TRSSA(Tent Random Walk Sparrow Optimization Algorithm)性能进行了验证,相比于SSA(Sparrow Optimization Algorithm)、AO(Aquila Optimizer)、POA(Pelican Optimization Algorithm)和GWO(Grey Wolf Optimizer),TRSSA算法具有更好的收敛速度、精度和统计性质。最后,由于ELM(Extreme Learning Machine)模型随机生成权重和阈值,降低了预测精度和泛化能力,应用TRSSA算法优化ELM模型的权重和阈值,并用三安光电股票数据集对TRSSA-ELM模型进行了测试。实验结果表明,TRSSA-ELM模型相比于SSA-ELM、ELM、SVR(Support Vector Regression)和GBDT(Gradient Boosting Decision Tree),具有更好的预测精度和稳定性。In order to solve the problems of uncertainty,discontinuity,randomness and nonlinearity in stock price forecasting,a TRSSA-ELM(Tent Random Walk Sparrow Optimization Algorithm-Extreme Learning Machine) stock price forecasting model is proposed.Firstly,adaptive Tent chaotic mapping and random walk strategy are used to improve the algorithm,which enhances the diversity and randomness of the population and improves the local and global optimization ability of the algorithm.Secondly,the performance of TRSSA(Tent Random Walk Sparrow Optimization Algorithm) is verified by using single peak,multi-peak and fixed multi-peak test functions.Compared to SSA(Sparrow Optimization Algorithm),AO(Aquila Optimizer),POA(Pelican Optimization Algorithm) and GWO(Grey Wolf Optimizer),TRSSA algorithm has better convergence speed,accuracy and statistical properties.Finally,because the ELM(Extreme Learning Machine) model randomly generates weights and thresholds,which reduces the prediction accuracy and generalization ability,TRSSA algorithm is applied to optimize the weights and thresholds of the ELM model,and the TRSSA-ELM model is tested in Sanan Optoelectronic stock data set.The experimental results show that TRSSA-ELM model has better prediction accuracy and stability than SSA-ELM,ELM,SVR(Support Vector Regression) and GBDT(Gradient Boosting Decision Tree).

关 键 词：股价预测 TRSSA-ELM预测模型 自适应Tent混沌映射 随机游走策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F830.91[自动化与计算机技术—控制科学与工程]