Establishing a direct connection between detrended fluctuation analysis and Fourier analysis

TL;DR

This paper analytically connects detrended fluctuation analysis (DFA) with Fourier analysis, revealing how DFA's scaling exponents relate to spectral properties and how polynomial order affects detection limits and scale distortion.

Contribution

It establishes an exact analytical relationship between DFA and Fourier analysis, clarifies the effects of polynomial detrending order, and proposes a corrected time scale to improve DFA accuracy.

Findings

Higher-order DFA does not affect the scaling exponent estimation for power-law PSDs.

The maximum detectable scaling exponent depends on the polynomial order used in DFA.

Introducing a corrected time scale reduces scale distortion in DFA analysis.

Abstract

To understand methodological features of the detrended fluctuation analysis (DFA) using a higher-order polynomial fitting, we establish the direct connection between DFA and Fourier analysis. Based on an exact calculation of the single-frequency response of the DFA, the following facts are shown analytically: (1) in the analysis of stochastic processes exhibiting a power-law scaling of the power spectral density (PSD), $S(f) \sim f^{-\beta}$, a higher-order detrending in the DFA has no adverse effect in the estimation of the DFA scaling exponent $\alpha$, which satisfies the scaling relation $\alpha = (\beta+1)/2$; (2) the upper limit of the scaling exponents detectable by the DFA depends on the order of polynomial fit used in the DFA, and is bounded by $m + 1$, where $m$ is the order of the polynomial fit; (3) the relation between the time scale in the DFA and the corresponding frequency in the PSD are distorted depending on both the order of the DFA and the frequency dependence of the PSD. We can improve the scale distortion by introducing the corrected time scale in the DFA corresponding to the inverse of the frequency scale in the PSD. In addition, our analytical approach makes it possible to characterize variants of the DFA using different types of detrending. As an application, properties of the DFA using moving average filtering are discussed.