Wavelet-Based Estimation of Hurst Exponent Using Neural Network

被引：2
|
作者：
Kirichenko, Lyudmyla [1 ,2 ]
Pavlenko, Kyrylo [1 ]
Khatsko, Daryna [1 ]
机构：
[1] Kharkiv Natl Univ Radioelect, Appl Math Dept, Kharkiv, Ukraine
[2] Wroclaw Univ Sci & Technol, Appl Math Dept, Wroclaw, Poland
来源：
2022 IEEE 17TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT) | 2022年
关键词：
Hurst exponent estimation; fractional Brownian motion; regression neural network; wavelet-based estimation;
D O I：
10.1109/CSIT56902.2022.10000906
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper proposes a method for estimating the Hurst exponent of time realizations using a regression neural network. The basis was the wavelet estimation method using a discrete wavelet transform. Wavelet energy spectra of fractional Brownian motion realizations were fed to the input of the neural network. The results showed that the accuracy of the Hurst exponent estimation, performed using a neural network, is ten times higher than the accuracy of statistical wavelet-based estimation.
引用
收藏
页码：40 / 43
页数：4
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