Please use this identifier to cite or link to this item: http://openarchive.nure.ua/handle/document/6363
Title: Hurst Exponent as a Part of Wavelet Decomposition Coefficients to Measure Long-term Memory Time Series Based on Multiresolution Analysis
Authors: Lyashenko, V.
Matarneh, R.
Baranova, V. V.
Deineko, Z.
Keywords: wavelet decomposition
Hurst exponent
Issue Date: 2016
Publisher: SciEP
Citation: Lyashenko V., Matarneh R., Baranova V., Deineko Z. Hurst Exponent as a Part of Wavelet Decomposition Coefficients to Measure Long-term Memory Time Series Based on Multiresolution Analysis // American Journal of Systems and Software. – 2016. – Vol. 4(2). – P. 51-56.
Abstract: Processing and analysis of data sequences using wavelet-decomposition and subsequent analysis of the all relevant coefficients of such decomposition is one of strong methods to study various processes and phenomena. The key point of data sequence analysis lies in the concept of Hurst exponent. This is due to the fact that Hurst exponent gives an indication of the complexity and dynamics of the correlation structure of any given time series taking into consideration the importance of Hurst exponent estimation for such analysis. There are various methods and approaches to find the Hurst exponent estimation with varying degrees of accuracy and complexity. Therefore, in this paper we have made an attempt to prove the possibility of considering an estimation of Hurst exponent based on the properties of coefficients of wavelet decomposition of a given time series. The obtained results which mainly based on the properties of detailing coefficients of wavelet decomposition show that estimation is easy to calculate and comparable with classic estimation of Hurst exponent. Also ratios has been obtained, that allow to analyze the self-similarity of a given time series.
URI: http://openarchive.nure.ua/handle/document/6363
ISSN: 2372-708X
Appears in Collections:Кафедра інформатики (ІНФ)

Files in This Item:
File Description SizeFormat 
ajss-4-2-4.pdf373.08 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.