Minimizing the effect of trends on detrended fluctuation analysis of long-range correlated noise

Detrended fluctuation analysis (DFA) has been proposed as a robust technique to determine possible long-range correlations in power-law processes [1]. However, recent studies have reported the susceptibility of DFA to trends [2] which give rise to spurious crossovers and prevent reliable estimation of the scaling exponents. Inspired by these reports, we propose a technique based on singular value-decomposition (SVD) of the trajectory matrix to minimize the effect of linear, power-law, periodic and also quasiperiodic trends superimposed on long-range correlated powerlaw noise. The effectiveness of the technique is demonstrated on publicly available data sets [2]. PACS Code : 05.40.-a