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
相关论文
共 50 条
  • [21] Estimation of Network Traffic Hurst Parameter Using HHT and Wavelet Transform
    Cheng, Xiaorong
    Xie, Kun
    Wang, Dong
    [J]. 2009 5TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-8, 2009, : 4308 - 4311
  • [22] Fault classification based on adaptive wavelet-based neural network
    Zhao, Z.
    Chen, W.
    Lin, Y.
    Chen, T.
    [J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2001, 21 (01): : 21 - 25
  • [23] Automatic recognition of vigilance state by using a wavelet-based artificial neural network
    Abdulhamit Subasi
    M. Kemal Kiymik
    Mehmet Akin
    Osman Erogul
    [J]. Neural Computing & Applications, 2005, 14 : 45 - 55
  • [24] Automatic recognition of vigilance state by using a wavelet-based artificial neural network
    Subasi, A
    Kiymik, MK
    Akin, M
    Erogul, O
    [J]. NEURAL COMPUTING & APPLICATIONS, 2005, 14 (01): : 45 - 55
  • [25] Estimating Hurst exponent with wavelet packet
    Wang, Zhiguo
    Guo, Dechun
    Li, Xi
    Fei, Yuanchun
    [J]. 7TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED INDUSTRIAL DESIGN & CONCEPTUAL DESIGN, 2006, : 105 - +
  • [26] Morlet Wavelet-Based Voice Liveness Detection using Convolutional Neural Network
    Gupta, Priyanka
    Chodingala, Piyushkumar K.
    Patil, Hemant A.
    [J]. 2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022), 2022, : 100 - 104
  • [27] On optimal scale upper bound in wavelet-based estimation for hurst index of fractional Brownian motion
    Kawasaki, Shuhji
    Morita, Hiroyoshi
    [J]. JOURNAL OF INTERDISCIPLINARY MATHEMATICS, 2005, 8 (02) : 195 - 214
  • [28] Estimation of Hurst exponent revisited
    Mielniczuk, J.
    Wojdyllo, P.
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2007, 51 (09) : 4510 - 4525
  • [29] WAVELET-BASED ESTIMATOR FOR THE HURST PARAMETERS OF FRACTIONAL BROWNIAN SHEET
    Wu, Liang
    Ding, Yiming
    [J]. ACTA MATHEMATICA SCIENTIA, 2017, 37 (01) : 205 - 222
  • [30] WAVELET-BASED ESTIMATOR FOR THE HURST PARAMETERS OF FRACTIONAL BROWNIAN SHEET
    吴量
    丁义明
    [J]. Acta Mathematica Scientia, 2017, (01) : 205 - 222
12345