Title: Hurst exponent estimation using neural network

Authors: Somenath Mukherjee; Bikash Sadhukhan; Arghya Kusum Das; Abhra Chaudhuri

Addresses: Nazrul Center of Social and Cultural Studies, Kazi Nazrul University, Asansol, West Bengal, India ' Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, India ' Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, India ' Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, India

Abstract: The Hurst exponent is used to identify the autocorrelation structure of a stochastic time series, which allows for detecting persistence in time series data. Traditional signal processing techniques work reasonably well in determining the Hurst exponent of a stochastic time series. However, a notable drawback of these methods is their speed of computation. Neural networks have repeatedly proven their ability to learn very complex input-output mappings, even in high dimensional vector spaces. Therefore, an endeavour has been undertaken to employ neural networks to determine the Hurst exponent of a stochastic time series. Unlike previous attempts to solve such problems using neural networks, the proposed architecture can be recognised as the universal estimator of Hurst exponent for short-range and long-range dependent stochastic time series. Experiments demonstrate that if sufficiently trained, neural network can predict the Hurst exponent of any stochastic data at least fifteen times faster than standard signal processing approaches.

Keywords: neural network; regression; Hurst exponent estimation; stochastic time series; long-range dependence; LRD; short-range dependence; SRD.

DOI: 10.1504/IJCSE.2023.129734

International Journal of Computational Science and Engineering, 2023 Vol.26 No.2, pp.157 - 170

Received: 30 Jul 2021
Accepted: 03 Nov 2021
Published online: 22 Mar 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article