Detrended fluctuation analysis
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April 2015

DFA.jl: Detrended fluctuation analysis in Julia



The DFA package provides tools to perform a detrended fluctuation analysis (DFA) and estimates the scaling exponent from the results. DFA is used to characterize long memory dependence in stochastic fractal time series.


To install the package:

julia> Pkg.clone("git@github.com:afternone/DFA.jl.git")

Usage Examples

We'll perform a DFA and estimates the scaling exponent for a random time series.

using DFA

x = rand(10000)
n, Fn = dfa(x)

You can also specify the following key arguments:

  • order: the order of the polynomial fit. Default: 1.
  • overlap: the overlap of blocks in partitioning the time data expressed as a fraction in [ 0,1). A positive overlap will slow down the calculations slightly with the (possible) effect of generating less biased results. Default: 0.
  • boxmax: an integer denoting the maximum block size to use in partitioning the data. Default: div(length(x), 2).
  • boxmin: an integer denoting the minimum block size to use in partitioning the data. Default: 2*(order+1).
  • boxratio: the ratio of successive boxes. This argument is used as an input to the logScale function. Default: 2.

To perform a DFA on x with boxmax=1000, boxmin=4, boxratio=1.2, overlap=0.5:

n1, Fn1 = dfa(x, boxmax=1000, boxmin=4, boxratio=1.2, overlap=0.5)

To estimates the scaling exponent:

intercept, α = polyfit(log10(n1), log10(Fn1))  # α is scaling exponent

To plot F(n)~n:

using PyPlot

loglog(n1, Fn1, "o")

To plot F(n)~n with fitted line:

logn1 = log10(n1)
plot(logn1, log10(Fn1), "o", logn1, α.*logn1.+intercept)


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