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Remarks on evaluation of correlation dimension for 5 French stock data

机译:评估5个法国股票数据的相关维数的备注

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Fractal correlation dimension (D/sub 2/) was introduced by Grassberger and Procaccia (1983) when considering some deterministic models originating from differential equations. Since then D/sub 2/ was applied in many situations; it has become also a tool in statistical data analysis of data burdened with noise. The presented paper attempts to emphasize the difficulties and uncertainties met when calculating correlation dimension for real time data observed as time series. In such situation the correlation dimension method works usually with an embedding space of dimension m. We see here two serious problems: 1) for given data, how to choose the embedding space; and 2) what is the accuracy of the characteristics D/sub 2/ obtained from the embedded data. The problems are illustrated considering time series of 5 French stock data, for which, despite all the restraints, we obtain reasonable results permitting to distinguish between stocks differentiated dynamics.
机译:分形相关维数(D / sub 2 /)由Grassberger和Procaccia(1983)在考虑一些源自微分方程的确定性模型时引入。从那时起,D / sub 2 /被应用于许多情况。它也已成为对噪声负荷较大的数据进行统计数据分析的工具。本文试图强调在计算作为时间序列的实时数据的相关维数时遇到的困难和不确定性。在这种情况下,相关维数方法通常适用于维数为m的嵌入空间。我们在这里看到两个严重的问题:1)对于给定的数据,如何选择嵌入空间; 2)从嵌入数据获得的特性D / sub 2 /的精度是多少。考虑到5个法国股票数据的时间序列,对这些问题进行了说明,尽管有很多限制,但对于这些时间序列,我们获得了合理的结果,可以区分不同的股票动态。

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