自相似数据流的Hurst指数小波求解法分析

ON WAVELET-BASED METHODS FOR HURST INDEX ESTIMATION OF SELF-SIMILAR TRAFFIC

该文针对具有自相似性的数据流的特征参数——Hurst指数的小波提取方法进行了系统的分析和研究,对现有提取Hurst指数的小波方法给予了归纳和总结。通过大量的实验和分析,研究了消失矩阶数、小波函数以及分解级数的选择对小波法提取Hurst指数的准确性的影响,并得出了一些有意义的结论。

Existing wavelet methods for the estimation of the Hurst parameter of self-similar traffic are systematically analyzed and examined. The effects of wavelet functions, vanishing moments and wavelet decomposition levels on the results of wavelet methods for acquiring the Hurst index are investigated via numerical experiments. Some useful conclusions are drawn on the relationship between the accuracy of the methods and the selection of the order of vanishing moments and the selection of wavelet functions.