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dc.contributor.authorDong, Huai Linzh_CN
dc.contributor.authorHuang, Juan Juanzh_CN
dc.contributor.authorCai, Zhu Huazh_CN
dc.contributor.authorWu, Qing Fengzh_CN
dc.contributor.author董槐林zh_CN
dc.contributor.author吴清锋zh_CN
dc.date.accessioned2015-07-22T02:16:12Z
dc.date.available2015-07-22T02:16:12Z
dc.date.issued2013zh_CN
dc.identifier.citationAdvanced Materials Research, 2013,753-755:2875-2881zh_CN
dc.identifier.issn1022-6680zh_CN
dc.identifier.other20134116828487zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/85756
dc.descriptionConference Name:3rd International Conference on Advanced Engineering Materials and Technology, AEMT 2013. Conference Address: Zhangjiajie, China. Time:May 11, 2013 - May 12, 2013.zh_CN
dc.description.abstractThere is huge amount of data with complex uncertainty in the stock market. Meanwhile, efficient stock prediction is important in financial investment. This paper puts forward a classified and predicted model based on least squares support vector machine (LS-SVM) in the background of stock investment. This model preprocesses the input vector of stock indexes using the method of Wilcoxon symbols test and factor analysis, and determines the parameter of LS-SVM based on the genetic algorithm, after that classifies the stocks based on growth rate, then is trained using the stock sample. At last this paper verifies the model with the samples. It also presents a demo to predict the increasing trend of the stock. The result shows that this model owns favorable predicted ability with high correct classification rate. ? (2013) Trans Tech Publications, Switzerland.zh_CN
dc.language.isoen_USzh_CN
dc.publisherTrans Tech Publications Ltdzh_CN
dc.source.urihttp://dx.doi.org/10.4028/www.scientific.net/AMR.753-755.2875zh_CN
dc.subjectGenetic algorithmszh_CN
dc.titleResearch on predicted model of least squares support vector machine based on genetic algorithmzh_CN
dc.typeConferencezh_CN


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