Description
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.
Papers Using This Method
InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis2025-06-10Video-based marker-free tracking and multi-scale analysis of mouse locomotor activity and behavioral aspects in an open field arena: A perspective approach to the quantification of complex gait disturbances associated with Alzheimer's disease2023-02-02Comparison between Time Shifting Deviation and Cross-correlation Methods2021-11-15Joint multifractal analysis based on the partition function approach: Analytical analysis, numerical simulation and empirical application2015-09-20Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces2015-04-15Detrended Partial-Cross-Correlation Analysis: A New Method for Analyzing Correlations in Complex System2015-01-30Mosaic organization of DNA nucleotides1994-02-01