Multifractal detrended fluctuation analysis of nonstationary time series
AbstractWe develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFA method to the standard partition function-based multifractal formalism, and prove that both approaches are equivalent for stationary signals with compact support. By analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series. By comparing the multifractal DFA results for original series with those for shuffled series we can distinguish multifractality due to long-range correlations from multifractality due to a broad probability density function. We also compare our results with the wavelet transform modulus maxima method, and show that the results are equivalent.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 316 (2002)
Issue (Month): 1 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Multifractal formalism; Scaling; Nonstationarities; Time series analysis; Long-range correlations; Broad distributions; Detrended fluctuation analysis;
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