Relative Roughness: An Index for Testing the Suitability of the Monofractal Model

Fractal analyses have become very popular and have been applied on a wide variety of empirical time series. The application of these methods supposes that the monofractal framework can offer a suitable model for the analyzed series. However, this model takes into account a quite specific kind of fluctuations, and we consider that fractal analyses have been often applied to series that were completely outside of its relevance. The problem is that fractal methods can be applied to all types of series, and they always give a result, that one can then erroneously interpret in the context of the monofractal framework. We propose in this paper an easily computable index, the relative roughness, defined as the ratio between local and global variances, that allows to test for the applicability of fractal analyses. We show that relative roughness is confined within a limited range (between 1.21 and 0.12) for long-range correlated series. We propose some examples of empirical series that have been recently analyzed using fractal methods, but, with respect to their relative roughness, should not have been considered in the monofractal model. An acceptable level of relative roughness, however, is a necessary but not sufficient condition for considering series as long-range correlated. Specific methods should be used in complement for testing for the effective presence of long-range correlations in empirical series.