GENERALIZED APPROACH TO HURST EXPONENT ESTIMATING BY TIME SERIES

Lyudmyla Kirichenko

lyudmyla.kirichenko@nure.ua
Kharkiv National University of Radioelectronics, Deptartment of Applied Mathematics (Ukraine)

Tamara Radivilova


Kharkiv National University of Radioelectronics, Deptartment of Infocommunication Engineering (Ukraine)

Vitalii Bulakh


Kharkiv National University of Radioelectronics, Deptartment of Applied Mathematics (Ukraine)

Abstract

This paper presents a generalized approach to the fractal analysis of self-similar random processes by short time series. Several stages of the fractal analysis are proposed. Preliminary time series analysis includes the removal of short-term dependence, the identification of true long-term dependence and hypothesis test on the existence of a self-similarity property. Methods of unbiased interval estimation of the Hurst exponent in cases of stationary and non-stationary time series are discussed. Methods of estimate refinement are proposed. This approach is applicable to the study of self-similar time series of different nature.


Keywords:

self-similar stochastic process, time series, Hurst exponent

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Published
2018-02-28

Cited by

Kirichenko, L., Radivilova, T., & Bulakh, V. (2018). GENERALIZED APPROACH TO HURST EXPONENT ESTIMATING BY TIME SERIES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 8(1), 28–31. https://doi.org/10.5604/01.3001.0010.8639

Authors

Lyudmyla Kirichenko 
lyudmyla.kirichenko@nure.ua
Kharkiv National University of Radioelectronics, Deptartment of Applied Mathematics Ukraine

Authors

Tamara Radivilova 

Kharkiv National University of Radioelectronics, Deptartment of Infocommunication Engineering Ukraine

Authors

Vitalii Bulakh 

Kharkiv National University of Radioelectronics, Deptartment of Applied Mathematics Ukraine

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