GENERALIZED APPROACH TO HURST EXPONENT ESTIMATING BY TIME SERIES

Abry P., Flandrin P., Taqqu M.S., Veitch D.: Self-similarity and long-range dependence through the wavelet lens. Theory and applications of long-range dependence, Birkhäuser 2003.
  Google Scholar

Abry P., Veitch D.: Wavelet analysis of long-range dependent traffic. IEEE/ACM Transactions Information Theory, Vol. 1(44), 1998.
  Google Scholar

Bassingthwaighte J.B., Liebovitch L.S., West B.J.: Fractal Physiology. Oxford University Press, New York 1994.
  Google Scholar

Ching E.S.C., Tsang Yue-Kin : Multifractality and scale invariance in human heartbeat dynamics. Physical Review E, Vol. 76, 2007, 041910.
  Google Scholar

Clegg R.G.: A practical guide to measuring the Hurst parameter. Computing science technical report 2005. Vol. CS–TR–916.
  Google Scholar

Cont R., Tankov P.: Financial modelling with jump processes. Chapman & Hall CRC Press, 2004.
  Google Scholar

Czarkowski M., Kaczmarek S., Wolff M.: Influence of Self-Similar Traffic Type on Performance of QoS Routing Algorithms. INTL Journal of electronics and telecommunications, Vol. 62, no. 1, 2016, 81–87.
  Google Scholar

Feder J.: Fractals. Plenum, New York 1988.
  Google Scholar

Flandrin P., Gonzalves P., Abry P.: Scaling, Fractals and Wavelets. John Wiley & Sons, London 2009.
  Google Scholar

Harikrishnan K.P., Misra R., Ambika G: Can the multifractal spectrum be used as a diagnostic tool? Chaotic Modeling and Simulation, Vol. 1, 2013, 51–57.
  Google Scholar

Hurst H.E., Black R.P., Simaila Y.M.: Long-Term Storage: An Experimental Study. Constable, London 1965.
  Google Scholar

Kantelhardt J.W., Koscielny-Bunde E., Rego H.H.A., Havlin S., Bunde A.: Detecting long-range correlations with detrended fluctuation analysis. Physica A 295, 2001, 441–454.
  Google Scholar

Kantelhardt J.W.: Fractal and Multifractal Time Series. 2008. http://arxiv.org/abs/0804.0747 (available: 12.10.2017).
  Google Scholar

Kirichenko L., Deineko Zh.: Estimation of the self-similarity of stochastic time series by the wavelet analysis method. Radio-electronic and computer systems, Vol. 4 (38), 2009, 99–105.
  Google Scholar

Kirichenko L., Radivilova T., Deineko Zh.: Comparative Analysis for Estimating of the Hurst Exponent for Stationary and Nonstationary Time Series. Information Technologies & Knowledge, Vol. 5, No 4, 2011, 371–388.
  Google Scholar

Kirichenko L., Radivilova T.: Comparative analysis of statistical properties of the Hurst exponent estimates obtained by different methods. Information Models of Knowledg. ITHEA, Kiev–Sofia 2010.
  Google Scholar

Kirichenko L., Radivilova T.: Investigation of long-term dependence of network traffic using R / S-analysis. Automated control systems and automation devices, Vol. 135, 2006, 51–55.
  Google Scholar

Meléndez G.R.: The fractal nature of bitcoin: evidence from wavelet power spectra. Fundacion Universidad de las Americas Puebla 39, 2014. http://ssrn.com/abstract=2547281 (available: 12.10.2017).
  Google Scholar

Peng C.-K., Buldyrev S.V., Havlin S., Simons M., Stanley H.E., Goldberger A.L.: Mosaic organization of DNA nucleotides. Phys. Rev. E 49, 1994, 1685–1689.
  Google Scholar

Peters E.E.: Fractal Market Analysis: applying chaos theory to investment and economics. Wiley, 2003.
  Google Scholar

Sheluhin O.I., Smolskiy S.M., Osin A.V.: Self-similar processes in telecommunications. JohnWiley & Sons Ltd, Chichester 2007.
  Google Scholar

Stollings W.: High-speed networks and Internets. Performance and quality of service. New Jersey 2002.
  Google Scholar

Tsugawa S., Ohsaki H.: Emergence of Fractals in Social Networks: Analysis of Community Structure and Interaction Locality. 38th Annual Computer Software and Applications Conference, 2014
  Google Scholar

Willinger W., Taqqu M.S., Erramilli A.A.: Bibliographical guide to self-similar traffic and performance modeling for modern high-speed network in ″Stohastic networks: theory and applications″. Claredon Press (Oxford University Press), Oxford 1996.
  Google Scholar