基于动态时间弯曲的股票时间序列联动性研究  

Co-movement Research of Stock Time Series Based on Dynamic Time Warping

作　　者：李海林[1] 梁叶[1] 

机构地区：[1]华侨大学信息管理系,泉州362021

出　　处：《数据采集与处理》2016年第1期117-129,共13页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61300139)资助项目;福建省中青年教育科研(JAS14024)资助项目;华侨大学中青年教师科研提升计划(ZQN-PY220)资助项目

摘　　要：对于股票联动性的研究,传统时间序列分析方法及目前数据挖掘技术主要使用国内或者国外股票指数来研究市场、板块或行业之间的联动关系,并得到一些较为宏观的结论,存在着缺少直接分析与挖掘个股数据之间的联动性的问题。鉴于此,本文提出一种基于动态时间弯曲的股票时间序列联动性研究方法。通过动态时间弯曲找出若干只形态相似的股票,并在此基础上获得相关的重要信息,再提出基于动态时间弯曲的k-means聚类方法实现股票聚类,进而得到具有相同波动趋势的股票簇。实验结果表明,新方法能从大量股票中准确找到具有联动关系的个股,区分开不同波动趋势的股票簇,具有一定的优越性。To investigate the co-movement of stock, traditional time series analysis and data mining technology mainly use domestic or foreign stock index to study the co-movement between market, sector or industry, and obtain some macroscopic conclusion. Therefore, there is a lack of direct analysis and mining linkage between individual stocks data issues. A method based on dynamic time warping is proposed to analyze the co-movement between two individual stocks. It can find some similar stocks in shape and obtain relevant essential information from extralarge stocks. Combining with k-means clustering method based on dynamic time warping, the clustering method can gain some clusters which have the same fluctuation tendency. The results demonstrate that the proposed method can accurately find the stocks which have linkage relationship from large amounts of stocks, as well as separating clusters of different fluctuation of stocks. It shows that the proposed method has a certain superiority.

关 键 词：股票联动性 动态时间弯曲 K-MEANS聚类 平均序列 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]