  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 quasi-periodic trends
superimposed on long-range correlated power-law noise. The effectiveness of the
technique is demonstrated on publicly available data sets [2].
