计算机工程与应用2024，Vol.60Issue(11)：346-355,10.DOI:10.3778/j.issn.1002-8331.2303-0032
结构化最大间隔双支持向量机在股票预测中的应用
Structured Maximum Margin Twin Support Vector Machine and Its Application in Stock Trend Prediction
摘要
Abstract
The stock price is affected by many factors,such as policy,macro-economy and the company's operating condi-tions,among which there is a certain degree of correlation.So the stock data of high noise and non-stationary feature makes stock prediction difficult.Based on the separability between classes of Mahalanobis distance,this paper proposes structured maximum margin twin suport vector machine(SMM-TWSVM).The method finds two nonparallel hyperplane for positive class samples and negative class samples respectively,so that the Euclidean distance of each class of samples from their own hyperplane is as small as possible,and the Mahalanobis distance of divorced class hyperplane is as large as possible.The experimental results of 8 benchmark datasets show that this method has a stable accuracy in the classifica-tion of noisy data,thus improving the prediction performance and anti-noise ability of the model.Meanwhile,it is applied to the prediction of the fluctuation tend of stock price,through the empirical analysis of 14 stocks such as Ping An of China and Shanghai Composite Index,Shanghai A Index,Shanghai 380 Index,the results show that compared with other com-parison models,SMM-TWSVM shows better prediction results and has certain practical value.关键词
分类问题/双支持向量机/数据结构/马氏距离/股票预测Key words
classification/twin support vector machine/data structure/Mahalanobis distance/stock prediction分类
信息技术与安全科学引用本文复制引用
林明松,杨晓梅,杨志霞..结构化最大间隔双支持向量机在股票预测中的应用[J].计算机工程与应用,2024,60(11):346-355,10.基金项目
国家自然科学基金(12061071). （12061071）
