Публікація:
Generalized approach to Hurst exponent estimating by time series

dc.contributor.authorKirichenko, L.
dc.contributor.authorRadivilova, T.
dc.contributor.authorBulakh, V.
dc.date.accessioned2018-06-04T12:24:26Z
dc.date.available2018-06-04T12:24:26Z
dc.date.issued2018
dc.description.abstractThis 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.citationKirichenko 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.28-31uk_UA
dc.identifier.issn2083-0157
dc.identifier.urihttp://openarchive.nure.ua/handle/document/5772
dc.language.isoenuk_UA
dc.relation.ispartofseries8 (1);
dc.subjectfractal analysisuk_UA
dc.subjectshort time seriesuk_UA
dc.subjectHurst exponenuk_UA
dc.titleGeneralized approach to Hurst exponent estimating by time seriesuk_UA
dc.typeArticleuk_UA
dspace.entity.typePublication

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