Detrended fluctuation analysis

In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes (diverging correlation time, e.g. power-law decaying autocorrelation function) or 1/f noise.

The obtained exponent is similar to the Hurst exponent, except that DFA may also be applied to signals whose underlying statistics (such as mean and variance) or dynamics are non-stationary (changing with time). It is related to measures based upon spectral techniques such as autocorrelation and Fourier transform.

Peng et al. introduced DFA in 1994 in a paper that has been cited over 2000 times as of 2013[1] and represents an extension of the (ordinary) fluctuation analysis (FA), which is affected by non-stationarities.

Calculation

Given a bounded time series x_t, t \in \mathbb{N}, integration or summation first converts this into an unbounded process X_t:

X_t=\sum_{i=1}^t (x_i-\langle x_i\rangle)

X_t is called cumulative sum or profile. This process converts, for example, an i.i.d. white noise process into a random walk.

Next, X_t is divided into time windows Y_j of length L samples, and a local least squares straight-line fit (the local trend) is calculated by minimising the squared error E^2 with respect to the slope and intercept parameters a, b:

E^2 = \sum_{j = 1}^L \left( Y_j - j a - b \right)^2.

Trends of higher order can be removed by higher order DFA, where the linear function j a +b is replaced by a polynomial of order n.[2] Next, the root-mean-square deviation from the trend, the fluctuation, is calculated over every window at every time scale:

F( L ) = \left[ \frac{1}{L}\sum_{j = 1}^L \left( Y_j - j a - b \right)^2 \right]^{\frac{1}{2}}.

This detrending followed by fluctuation measurement process is repeated over the whole signal at a range of different window sizes L, and a log-log graph of L against F(L) is constructed.

A straight line on this log-log graph indicates statistical self-affinity expressed as F(L) \propto L^{\alpha}. The scaling exponent \alpha is calculated as the slope of a straight line fit to the log-log graph of L against F(L) using least-squares. This exponent is a generalization of the Hurst exponent. Because the expected displacement in an uncorrelated random walk of length L grows like \sqrt{L}, an exponent of \tfrac{1}{2} would correspond to uncorrelated white noise. When the exponent is between 0 and 1, the result is Fractional Brownian motion, with the precise value giving information about the series self-correlations:

There are different orders of DFA. In the described case, linear fits (n=1) are applied to the profile, thus it is called DFA1. In general, DFAn, uses polynomial fits of order n. Due to the summation (integration) from x_i to X_t, linear trends in the mean of the profile represent constant trends in the initial sequence, and DFA1 only removes such constant trends (steps) in the x_i. In general DFA of order n removes (polynomial) trends of order n-1. For linear trends in the mean of x_i at least DFA2 is needed. The Hurst R/S analysis removes constants trends in the original sequence and thus, in its detrending it is equivalent to DFA1. The DFA method was applied to many systems; e.g., DNA sequences,[3][4] neuronal oscillations,[5] speech pathology detection,[6] and heartbeat fluctuation in different sleep stages.[7] Effect of trends on DFA were studied in[8] and relation to the power spectrum method is presented in.[9]

Since in the fluctuation function F(L) the square(root) is used, DFA measures the scaling-behavior of the second moment-fluctuations, this means \alpha=\alpha(2). The multifractal generalization (MF-DFA)[10] uses a variable moment q and provides \alpha(q). Kantelhardt et al. intended this scaling exponent as a generalization of the classical Hurst exponent. The classical Hurst exponent corresponds to the second moment for stationary cases H=\alpha(2) and to the second moment minus 1 for nonstationary cases H=\alpha(2)-1.[5][10]

Relations to other methods

In the case of power-law decaying auto-correlations, the correlation function decays with an exponent \gamma: C(L)\sim L^{-\gamma}\!\ . In addition the power spectrum decays as P(f)\sim f^{-\beta}\!\ . The three exponent are related by:[3]

The relations can be derived using the Wiener–Khinchin theorem.

Thus, \alpha is tied to the slope of the power spectrum \beta and is used to describe the color of noise by this relationship: \alpha = (\beta+1)/2.

For fractional Gaussian noise (FGN), we have \beta \in [-1,1] , and thus \alpha = [0,1], and \beta = 2H-1, where H is the Hurst exponent. \alpha for FGN is equal to H.

For fractional Brownian motion (FBM), we have \beta \in [1,3] , and thus \alpha = [1,2], and \beta = 2H+1, where H is the Hurst exponent. \alpha for FBM is equal to H+1. In this context, FBM is the cumulative sum or the integral of FGN, thus, the exponents of their power spectra differ by 2.

Pitfalls in interpretation

As with most methods that depend upon line fitting, it is always possible to find a number \alpha by the DFA method, but this does not necessarily imply that the time series is self-similar. Self-similarity requires that the points on the log-log graph are sufficiently collinear across a very wide range of window sizes L.

Also, there are many scaling exponent-like quantities that can be measured for a self-similar time series, including the divider dimension and Hurst exponent. Therefore, the DFA scaling exponent \alpha is not a fractal dimension sharing all the desirable properties of the Hausdorff dimension, for example, although in certain special cases it can be shown to be related to the box-counting dimension for the graph of a time series.

See also

References

  1. ↑ Peng, C.K. et al. (1994). "Mosaic organization of DNA nucleotides". Phys. Rev. E 49: 1685–1689. doi:10.1103/physreve.49.1685.
  2. ↑ Kantelhardt J.W. et al. (2001). "Detecting long-range correlations with detrended fluctuation analysis". Physica A 295: 441–454. doi:10.1016/s0378-4371(01)00144-3.
  3. ↑ 3.0 3.1 Buldyrev et al. (1995). "Long-Range Correlation-Properties of Coding And Noncoding Dna-Sequences- Genbank Analysis". Phys. Rev. E 51: 5084–5091. doi:10.1103/physreve.51.5084.
  4. ↑ Bunde A, Havlin S (1996). "Fractals and Disordered Systems, Springer, Berlin, Heidelberg, New York".
  5. ↑ 5.0 5.1 Hardstone, Richard; Poil, Simon-Shlomo; Schiavone, Giuseppina; Jansen, Rick; Nikulin, Vadim V.; Mansvelder, Huibert D.; Linkenkaer-Hansen, Klaus (1 January 2012). "Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations". Frontiers in Physiology 3. doi:10.3389/fphys.2012.00450.
  6. ↑ Little et al. (2006). "Nonlinear, Biophysically-Informed Speech Pathology Detection". 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings.: Toulouse, France. pp. II-1080-II-1083.
  7. ↑ Bunde A. et al. (2000). "Correlated and uncorrelated regions in heart-rate fluctuations during sleep". Phys. Rev. E 85 (17): 3736–3739. doi:10.1103/physrevlett.85.3736.
  8. ↑ Hu, K. et al. (2001). "Effect of trends on detrended fluctuation analysis". Phys. Rev. E 64 (1): 011114. doi:10.1103/physreve.64.011114.
  9. ↑ Heneghan et al. (2000). "Establishing the relation between detrended fluctuation analysis and power spectral density analysis for stochastic processes". Phys. Rev. E 62 (5): 6103–6110. doi:10.1103/physreve.62.6103.
  10. ↑ 10.0 10.1 H.E. Stanley, J.W. Kantelhardt; S.A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde (2002). "Multifractal detrended fluctuation analysis of nonstationary time series". Physica A 316: 87. doi:10.1016/s0378-4371(02)01383-3.

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