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Extended self-similarity based multi-fractal detrended fluctuation analysis: A novel multi-fractal quantifying method

机译:基于扩展自相似的多重分形趋势分析:一种新颖的多重分形量化方法

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Based on multi-fractal detrended fluctuation analysis (MF-DFA) method, the extended self-similarity (ESS) is incorporated to develop a novel method to quantify multi-fractal characteristics. Detailed results show that compared with MF-DFA this new extended self-similarity based MF-DFA (ESS-MF-DFA) method can significantly extend scaling range and reduce uncertainties in estimating the exponents. Moreover, although ESS-MF-DFA method is developed from the DFA method with a fundamental assumption of a definite scaling range between fluctuation function and scale, ESS-MF-DFA can still work well even when DFA fails due to no scaling range between fluctuation function and scale. Furthermore, a criterion without estimating the generalized Hurst exponents is developed based on ESS-MF-DFA to distinguish multi-fractal from mono-fractal behavior and to quantify multi-fractal strength. All these results indicate that ESS-MF-DFA outperforms MF-DFA in reliably handling multi-fractal quantifications for much wider fields. (C) 2018 Elsevier B.V. All rights reserved.
机译:基于多重分形去趋势波动分析(MF-DFA)方法,结合扩展自相似度(ESS)来开发一种量化多重分形特征的新方法。详细的结果表明,与MF-DFA相比,这种新的基于扩展自相似性的MF-DFA(ESS-MF-DFA)方法可以显着扩展缩放范围,并减少估计指数的不确定性。此外,尽管ESS-MF-DFA方法是从DFA方法发展而来的,基本假设是波动函数和标度之间的比例范围确定,但即使由于波动之间没有标度范围而导致DFA失败时,ESS-MF-DFA仍然可以正常工作功能和规模。此外,基于ESS-MF-DFA,开发了一种无需估计广义Hurst指数的判据,以区分多重分形与单一分形行为,并量化多重分形强度。所有这些结果表明,ESS-MF-DFA在可靠地处理更广泛领域的多重分形量化方面优于MF-DFA。 (C)2018 Elsevier B.V.保留所有权利。

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