###
计算机系统应用:2020,29(2):212-218
←前一篇   |   后一篇→
本文二维码信息
码上扫一扫!
基于改进回声状态神经网络的个股股价预测
(江苏大学 计算机科学与通信工程学院, 镇江 212013)
Forecast of Individual Stock Closing Price Based on Improved Echo State Neural Network
(School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 138次   下载 127
投稿时间:2019-07-13    修订日期:2019-08-22
中文摘要: 当今社会股价预测是研究的热门问题,人们越来越关注对股价预测模型的建立,提高股价预测的精度对股票投资者有实际的应用价值.目前股价的预测方法层出不穷,其中较为典型的有传统的技术分析和ARMA模型等.为了提升预测的精度,同时考虑到股市的非线性,本文提出一种改进的回声状态神经网络的个股股价预测模型,针对回声状态神经网络(ESN)泛化能力不强的特点,应用改进的粒子群算法(GTPSO)对回声状态神经网络(ESN)的输出连接权进行搜索,最终得到最优解,即ESN的最优输出连接权,GTPSO算法概括来说就是在传统粒子群算法(PSO)的基础上引入禁忌搜索算法(TS)中禁忌的思想和遗传算法(GA)中变异的思想,从而降低PSO在学习过程中陷入局部最小值的状况,同时提高PSO搜寻全局的能力.将预测模型用于个股每日收盘价预测中,使用每10天的收盘价预测第11天的收盘价.通过实验验证了模型的正确性,实验证实,该模型拥有较好的预测效果.
中文关键词: GA  个股股价  ESN  PSO  TS
Abstract:The current stock price forecast is a hot issue in research. People are paying more and more attention to the establishment of stock price forecasting model, and improving the accuracy of stock price forecast has practical application value for stock investors. At present, the forecasting methods of stock prices are endless, among which the typical ones are traditional technical analysis and ARMA models. In order to improve the accuracy of prediction and consider the nonlinearity of stock market, this study proposes an improved stock price forecasting model of echo state neural network. The improved particle is applied to the characteristics of Echo State Neural Network (ESN). The group algorithm (GTPSO) searches the output connection weight of the ESN, and finally obtains the optimal solution, i.e., the optimal output connection weight of the ESN. The GTPSO algorithm is generally in the traditional Particle Swarm Optimization (PSO) algorithm. Based on the idea of taboo in the Tabu Search algorithm (TS) and the idea of mutation in the Genetic Algorithm (GA), the PSO is reduced to a local minimum during the learning process, and the ability of the PSO to search globally is improved. The forecasting model is used in the daily closing price forecast of individual stocks, and the closing price of the 11th day is predicted using the closing price of every 10 days. The correctness of the model is verified by experiments, and it is proved that the model has a good prediction effect.
keywords: GA  individual stock prices  ESN  PSO  TS
文章编号:     中图分类号:    文献标志码:
基金项目:
引用文本:
李莉,程露.基于改进回声状态神经网络的个股股价预测.计算机系统应用,2020,29(2):212-218
LI Li,CHENG Lu.Forecast of Individual Stock Closing Price Based on Improved Echo State Neural Network.COMPUTER SYSTEMS APPLICATIONS,2020,29(2):212-218

用微信扫一扫

用微信扫一扫