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.
DFA was introduced by Peng et al. 1994 and represents an extension of the (ordinary) fluctuation analysis (FA), which is affected by non-stationarities.
Read more about Detrended Fluctuation Analysis: Calculation, Relations To Other Methods, Pitfalls in Interpretation
Famous quotes containing the word analysis:
“A commodity appears at first sight an extremely obvious, trivial thing. But its analysis brings out that it is a very strange thing, abounding in metaphysical subtleties and theological niceties.”
—Karl Marx (1818–1883)