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The method of detrended fluctuation analysis (DFA) has proven useful in revealing the extent of long-range correlations in seemingly irregular time series.
Briefly, the time series to be analyzed is first integrated. Next, the integrated time series is divided into boxes of equal length, n. In each box of length n, a least squares line (or polynomial curve of order k) is fit to the data (representing the trend in that box). Next, we detrend the integrated time series by subtracting the local trend in each box. The root-mean-square fluctuation of this integrated and detrended time series is calculated and denoted as F(n) .
This computation is repeated over all time scales (box sizes), from n = minbox to n = maxbox, to characterize the relationship between F(n) , the average fluctuation, and n, the box size. Typically, F(n) will increase with box size n. A linear relationship on a log-log plot indicates the presence of power law (fractal) scaling. Under such conditions, the fluctuations can be characterized by a scaling exponent, i.e., the slope of the line relating log[F(n) ] to log[n].
This program performs detrended fluctuation analysis on a sequence of data read from the standard input (which should contain a single column of numbers in text format). The standard output contains two columns of numbers, which are the base 10 logarithms of n and F(n) . Note that dfa does not compute a scaling exponent; to do so, fit the output to a line and measure its slope.
Options may include:
The DFA method was first proposed in Peng C-K, Buldyrev SV, Havlin S, Simons M, Stanley HE, Goldberger AL. Mosaic organization of DNA nucleotides. Phys Rev E 1994;49:1685-1689.
A detailed description of the algorithm and its application to physiologic signals can be found in Peng C-K, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995;5:82-87.
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Updated 8 March 2019