Reliable scaling exponent estimation of long-range correlated noise in the presence of random spikes

TL;DR

This paper introduces a singular-value decomposition (SVD) filtering method to improve the accuracy of scaling exponent estimation in long-range correlated noise contaminated with random spikes, enhancing DFA analysis reliability.

Contribution

The paper proposes a novel SVD-based filtering technique to mitigate the impact of random spikes on DFA scaling exponent estimates in long-range correlated data.

Findings

SVD filter effectively reduces spike effects in simulated data.

Improved accuracy of scaling exponent estimation demonstrated.

Method applicable to different spike distributions.

Abstract

Detrended fluctuation analysis (DFA) has been used widely to determine possible long-range correlations in data obtained from diverse settings. In a recent study [1], uncorrelated random spikes superimposed on the long-range correlated noise (LR noise) were found to affect DFA scaling exponent estimates. In this brief communication, singular-value decomposition (SVD) filter is proposed to minimize the effect random spikes superimposed on LR noise, thus facilitating reliable estimation of the scaling exponents. The effectiveness of the proposed approach is demonstrated on random spikes sampled from normal and uniform distributions.