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 3,000 times as of 2022 and represents an extension of the (ordinary) fluctuation analysis (FA), which is affected by non-stationarities. Definition

Algorithm 
Given: a time series . Compute its average value . Sum it into a process . This is the cumulative sum, or profile, of the original time series. For example, the profile of an i.i.d. white noise is a standard random walk. Select a set  of integers, such that , the smallest , the largest , and the sequence is roughly distributed evenly in log-scale: . In other words, it is approximately a geometric progression. For each , divide the sequence  into consecutive segments of length . Within each segment, compute the least squares straight-line fit (the local trend). Let  be the resulting piecewise-linear fit. Compute the root-mean-square deviation from the local trend (local fluctuation):And their root-mean-square is the total fluctuation:

(If  is not divisible by , then one can either discard the remainder of the sequence, or repeat the procedure on the reversed sequence, then take their root-mean-square.) Make the log-log plot . Interpretation 
A straight line of slope  on the log-log plot indicates a statistical self-affinity of form . Since  monotonically increases with , we always have . The scaling exponent  is a generalization of the Hurst exponent, with the precise value giving information about the series self-correlations:

 : anti-correlated
 : uncorrelated, white noise
 : correlated
 : 1/f-noise, pink noise
 : non-stationary, unbounded
 : Brownian noise
Because the expected displacement in an uncorrelated random walk of length N grows like , an exponent of  would correspond to uncorrelated white noise. When the exponent is between 0 and 1, the result is fractional Gaussian noise. Pitfalls in interpretation 
Though the DFA algorithm always produces a positive number  for any time series, it does not necessarily imply that the time series is self-similar. Self-similarity requires the log-log graph to be sufficiently linear over a wide range of . Furthermore, a combination of techniques including MLE, rather than least-squares has been shown to better approximate the scaling, or power-law, exponent. 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  is not a fractal dimension, and does not have certain desirable properties that the Hausdorff dimension has, though in certain special cases it is related to the box-counting dimension for the graph of a time series. Generalizations

Generalization to polynomial trends (higher order DFA)
The standard DFA algorithm given above removes a linear trend in each segment. If we remove a degree-n polynomial trend in each segment, it is called DFAn, or higher order DFA. Since  is a cumulative sum of , a linear trend in  is a constant trend in , which is a constant trend in  (visible as short sections of "flat plateaus"). In this regard, DFA1 removes the mean from segments of the time series  before quantifying the fluctuation. Similarly, a degree n trend in  is a degree (n-1) trend in . For example, DFA1 removes linear trends from segments of the time series  before quantifying the fluctuation, DFA1 removes parabolic trends from , and so on. The Hurst R/S analysis removes constant trends in the original sequence and thus, in its detrending it is equivalent to DFA1. Generalization to different moments (multifractal DFA) 
DFA can be generalized by computingthen making the log-log plot of , If there is a strong linearity in the plot of , then that slope is . DFA is the special case where . Multifractal systems scale as a function . Essentially, the scaling exponents need not be independent of the scale of the system. In particular, DFA measures the scaling-behavior of the second moment-fluctuations. Kantelhardt et al. intended this scaling exponent as a generalization of the classical Hurst exponent. The classical Hurst exponent corresponds to  for stationary cases, and  for nonstationary cases. Applications 

The DFA method has been applied to many systems, e.g. DNA sequences, neuronal oscillations, speech pathology detection, heartbeat fluctuation in different sleep stages, and animal behavior pattern analysis. The effect of trends on DFA has been studied. Relations to other methods, for specific types of signal

For signals with power-law-decaying autocorrelation 

In the case of power-law decaying auto-correlations, the correlation function decays with an exponent :
. In addition the power spectrum decays as . The three exponents are related by:
 
  and
 . The relations can be derived using the Wiener–Khinchin theorem. The relation of DFA to the power spectrum method has been well studied. Thus,  is tied to the slope of the power spectrum  and is used to describe the color of noise by this relationship: . For fractional Gaussian noise 
For fractional Gaussian noise (FGN), we have , and thus , and , where  is the Hurst exponent. for FGN is equal to . For fractional Brownian motion 

For fractional Brownian motion (FBM), we have , and thus , and , where  is the Hurst exponent. for FBM is equal to . In this context, FBM is the cumulative sum or the integral of FGN, thus, the exponents of their
power spectra differ by 2. See also
 Multifractal system
 Self-organized criticality
 Self-affinity
 Time series analysis
 Hurst exponent

References

External links 
  Tutorial on how to calculate detrended fluctuation analysis in Matlab using the Neurophysiological Biomarker Toolbox. FastDFA MATLAB code for rapidly calculating the DFA scaling exponent on very large datasets. Physionet A good overview of DFA and C code to calculate it. MFDFA Python implementation of (Multifractal) Detrended Fluctuation Analysis. Autocorrelation
Fractals