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@Article{CampanharoRamo:2016:HuExEs,
               author = "Campanharo, Adriana S. L. O. and Ramos, Fernando Manuel",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)}",
                title = "Hurst exponent estimation of self-affine time series using 
                         quantile graphs",
              journal = "Physica A: Statistical Mechanics and its Applications",
                 year = "2016",
               volume = "444",
                pages = "43--48",
                month = "Feb.",
             keywords = "Self-affine time series, Hurst exponent, Complex networks, 
                         Quantile graphs.",
             abstract = "In the context of dynamical systems, time series analysis is 
                         frequently used to identify the underlying nature of a phenomenon 
                         of interest from a sequence of observations. For signals with a 
                         self-affine structure, like fractional Brownian motions (fBm), the 
                         Hurst exponent H is one of the key parameters. Here, the use of 
                         quantile graphs (QGs) for the estimation of H is proposed. A QG is 
                         generated by mapping the quantiles of a time series into nodes of 
                         a graph. H is then computed directly as the power-law scaling 
                         exponent of the mean jump length performed by a random walker on 
                         the QG, for different time differences between the time series 
                         data points. The QG method for estimating the Hurst exponent was 
                         applied to fBm with different H values. Comparison with the exact 
                         H values used to generate the motions showed an excellent 
                         agreement. For a given time series length, estimation error 
                         depends basically on the statistical framework used for 
                         determining the exponent of the power-law model. The QG method is 
                         numerically simple and has only one free parameter, Q, the number 
                         of quantiles/nodes. With a simple modification, it can be extended 
                         to the analysis of fractional Gaussian noises.",
                  doi = "10.1016/j.physa.2015.09.094",
                  url = "http://dx.doi.org/10.1016/j.physa.2015.09.094",
                 issn = "0378-4371",
             language = "en",
           targetfile = "campanharo_hurst.pdf",
        urlaccessdate = "27 nov. 2020"
}


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