| 股票时间序列模型的关联规则挖掘 |
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| 引用本文： | 张娟,王慧锋.股票时间序列模型的关联规则挖掘[J].天津理工大学学报,2006,22(2):35-38. |
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| 作者姓名： | 张娟 王慧锋 |
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| 作者单位： | 华东理工大学,信息学院,上海,200237 |
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| 摘 要： | 目前的数据挖掘技术偏重于发现类似于商业销售数据库中不同离散化属性值之间的关系,而对证券投资中数值型数据之间变化趋势的相互影响分析不够.以股票信息的关联规则挖掘为例,大多采用传统的关联规则算法(如Apriori)来发现离散序列数据库中事务间的关系,时间序列关联规则挖掘的
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| 关 键 词： | 时间序列模型 关联规则 股票分析 |
| 文章编号： | 1673-095X（2006）02-0035-04 |
| 收稿时间： | 2005-10-17 |
| 修稿时间： | 2005年10月17 |
Association rules mining in stock time series model |
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| ZHANG Juan,WANG Hui-feng.Association rules mining in stock time series model[J].Journal of Tianjin University of Technology,2006,22(2):35-38. |
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| Authors: | ZHANG Juan WANG Hui-feng |
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| Institution: | Institute of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China |
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| Abstract: | In order to assist stock investors to make reasonable decision,it's required to lucubrate on association rules analysis.The classical researches focus on the mining of associated items within the same transaction record,thus have no forecasting value.To solve this problem,a time series model is built in this paper,which can express the associations within not only the same but also the different transaction records.Then we present two mining algorithms named Optimized Apriori and ES-Apriori that can optimize the inefficient Apriori.The model and algorithms are proved to be efficient and correct by experiments.The mining results can reflect the practical information in stock market,and can be helped to guide the investors. |
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| Keywords: | time series model association rules stock analysis |
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