Generalized approach to Hurst exponent estimating by time series

Публікація:
Generalized approach to Hurst exponent estimating by time series

dc.contributor.author	Kirichenko, L.
dc.contributor.author	Radivilova, T.
dc.contributor.author	Bulakh, V.
dc.date.accessioned	2018-06-04T12:24:26Z
dc.date.available	2018-06-04T12:24:26Z
dc.date.issued	2018
dc.description.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.	uk_UA
dc.identifier.citation	Kirichenko L. Generalized approach to Hurst exponent estimating by time series / Lyudmyla Kirichenko, Tamara Radivilova, Vitalii Bulakh // Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska, 2018, Volume 8, No. 1, pp.28-31	uk_UA
dc.identifier.issn	2083-0157
dc.identifier.uri	http://openarchive.nure.ua/handle/document/5772
dc.language.iso	en	uk_UA
dc.relation.ispartofseries	8 (1);
dc.subject	fractal analysis	uk_UA
dc.subject	short time series	uk_UA
dc.subject	Hurst exponen	uk_UA
dc.title	Generalized approach to Hurst exponent estimating by time series	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication