  We 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 to 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 (WTMM) method, and show that the results are equivalent.
