TTA, a new approach to estimate Hurst exponent with less estimation error and computational time

Hamze Lotfalinezhad and Ali Maleki

Physica A: Statistical Mechanics and its Applications, 2020, vol. 553, issue C

Abstract: Investigation of long memory processes in signals can give us an important information about how signals have behaved so far and how will it behave in future. Hurst exponent estimation is a proper tool to show memory in signals. Rescaled range analysis (R/S), detrended fluctuation analysis (DFA) and generalized Hurst exponent (GHE) are most known methods for estimation of Hurst exponent which introduced in literature. In this paper, we propose a new algorithm to estimate Hurst exponent based on triangles total areas (TTA) that can be made out of three samples of different lag in time series. To test our algorithm performance, we used two kinds of synthetic waveforms with known Hurst exponents. Results indicates that the proposed method is superior with respect to data length, estimation error, computational time and noise sensitivity. We also apply our proposed method in epilepsy detection and compare our results with previous works to show outperformance of our algorithm with accuracy of 94.5% in classification between interictal and ictal EEG signals.

Keywords: Hurst exponent; Long range dependence; Epilepsy detection (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119322605
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:553:y:2020:i:c:s0378437119322605

DOI: 10.1016/j.physa.2019.124093

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().