Minimizing the effect of periodic and quasi-periodic trends in detrended fluctuation analysis

Detrended fluctuation analysis (DFA) has been proposed as a robust technique to determine possible long-range correlations in power-law processes. However, recent studies have reported the susceptibility of DFA to periodic trends, which can result in spurious crossovers. In this brief report, we propose a technique based on singular value decomposition to minimize the effect of both periodic as well as quasi-periodic trends in DFA estimation. The effectiveness of the proposed technique is demonstrated on publicly available data sets.