基于方差波动多重分形特征的金融时间序列聚类被引量：13

Clustering Financial Time Series Based on Multi-fractal Features of Variance Volatility

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摘要提出了一种新的概率函数计算方法,用于研究金融时间序列在方差波动方面的多重分形特征。在此基础上提出了一种基于多重分形的时间序列聚类算法,该算法能够根据不同的分析目的,灵活地使用不同的概率函数以及序列的多重分形特征参量进行聚类。对上海证券市场实际数据的实验结果表明,本文提出的聚类算法是灵活有效的。 A new probabilistic function for studying the multi-fractal features on the volatility of variance of financial time series is proposed. Then a new time series clustering algorithm based on multi-fractal features is brought forward, which can cluster financial time series flexibly according to different purpose by using different probabilistic functions and multifractal features parameters. The experiments conducted on real data of Shanghai securities market show the algorithm is practical and effective.

作者黄超吴清烈武忠朱扬勇

机构地区东南大学经济管理学院复旦大学计算机与信息技术系

出处《系统工程》 CSCD 北大核心 2006年第6期100-103,共4页 Systems Engineering

基金上海市科委科技攻关计划项目(045115003)

关键词多重分形时间序列方差波动聚类 Multi-fractal Features Time Series Variance Volatility Clustering

分类号 F830 [经济管理—金融学]

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参考文献11

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