Detrended fluctuation analysis (DFA) is a scaling method commonly used for detecting long-range correlations in non-stationary time series. The DFA method uses a trend based on polynomial fitting to extract and quantify fluctuations at different time scales. Basically, such procedure acts as a...

In this work, the dynamical behavior of the US stock markets is characterized on the basis of the temporal variations of the Hurst exponent estimated with detrended fluctuation analysis (DFA) over moving windows for the historical Dow Jones (1928–2007) and the S&P-500 (1950–2007) daily...

Some regularities in popular marathon races are identified in this paper. It is found for high-performance participants (i.e., racing times in the range [2:15,3:15]h), the average velocity as a function of the marathoner's ranking behaves as a power-law, which may be suggesting the presence of...

The aim of this paper is to explore the application of detrended fluctuation analysis (DFA) to study roughness features of images. Unidimensional sequences at different image orientations are extracted and their average scaling exponent is estimated. In this form, the existence of anisotropies...

Price formation in crude oil markets is the result of the action of many participants (e.g., producers, governments, speculators, etc.) whose effects are perceived at different time scales, from days to years. The diversity of participants as well as the occurrence of extreme socio-political...

Here we propose a method, based on detrended fluctuation analysis, to investigate asymmetric correlations in nonstationary time series. The aim is to show that, for a certain range of time scales, different scaling properties are found if signal trending is either positive and negative. We...