Testing fractal connectivity in multivariate long memory processes
Testing fractal connectivity in multivariate long memory processes
复制标题
测试多元长记忆过程中的分形连通性
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
S. Achard
中科院分区：
文献类型：
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作者：
H. Wendt;A. Scherrer;P. Abry;S. Achard
在长记忆多元过程的框架内，分形连通性是一种特殊的模型，其中每对过程分量的互谱的低频（粗尺度）由分量的自谱确定。潜在的直觉是，每个组件中的长记忆很可能来自相同且单一的机制。目前的贡献旨在定义和表征用于测试数据之间实际分形连通性的统计程序。该测试基于 Fisher Z 变换和 Pearson 相关系数，并锚定在小波框架中。其性能经过理论分析并在合成数据上进行验证。它的有用性通过对互联网流量数据包和字节计数时间序列的分析来说明。
Within the framework of long memory multivariate processes, fractal connectivity is a particular model, in which the low frequencies (coarse scales) of the interspectrum of each pair of process components are determined by the autospectra of the components. The underlying intuition is that long memories in each components are likely to arise from a same and single mechanism. The present contribution aims at defining and characterizing a statistical procedure for testing actual fractal connectivity amongst data. The test is based on Fisher's Z transform and Pearson correlation coefficient, and anchored in a wavelet framework. Its performance are analyzed theoretically and validated on synthetic data. Its usefulness is illustrated on the analysis of Internet traffic Packet and Byte count time series.