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Cited by 27 publications
(21 citation statements)
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“…As well as it happens with other tools to test for scaling properties of time series (see for example, MF-DFA [ 76 ], MF-DMA [ 63 ] and FD4 [ 77 ]), GHE provides a valid procedure to deal with multi-fractal (also multi-scaling) processes, namely, those ones in which the scaling exponent H ( q ) depends on each moment q . In such case, different exponents characterize the scaling properties of different q th-moments for the underlying distribution [ 78 , 79 ].…”
Section: Methodsmentioning
confidence: 99%
“…As well as it happens with other tools to test for scaling properties of time series (see for example, MF-DFA [ 76 ], MF-DMA [ 63 ] and FD4 [ 77 ]), GHE provides a valid procedure to deal with multi-fractal (also multi-scaling) processes, namely, those ones in which the scaling exponent H ( q ) depends on each moment q . In such case, different exponents characterize the scaling properties of different q th-moments for the underlying distribution [ 78 , 79 ].…”
Section: Methodsmentioning
confidence: 99%
“…For the sake of verifying the fractal nature in the BS topology, the rescaled range analysis (R/S) method [17] is adopted to compute the Hurst coefficients of all the selected cities in this letter. Due to the space limitation, the details about the calculation of the Hurst coefficients will not be provided here, and all the relevant information can be found in [7].…”
Section: B Fractal Features Based On the Hurst Coefficientsmentioning
confidence: 99%
“…This makes a GHE based analysis quite simple to test for scaling properties on time series. As well as it happens with other approaches to test for scaling properties on series (see e.g., MF-DFA [17] as well as FD algorithms [8]), GHE provides an appropriate procedure to deal with multi-fractal (also multi-scaling) processes, namely, those ones for which the scaling exponent H(q) depends on each qth-order moment. In such a case, different exponents characterize the scaling properties for distinct qth-order moments on the underlying distribution [6,21].…”
Section: Generalized Hurst Exponentmentioning
confidence: 99%