Computing the multifractal spectrum from time series: An algorithmic approach

Abstract

We show that the existing methods for computing the f(α) spectrum from a time series can be improved by using a new algorithmic scheme. The scheme relies on the basic idea that the smooth convex profile of a typical f(α) spectrum can be fitted with an analytic function involving a set of four independent parameters. While the standard existing schemes [ P. Grassberger et al., J. Stat. Phys. 51, 135 (1988) ; A. Chhabra and R. V. Jensen, Phys. Rev. Lett. 62, 1327 (1989) ] generally compute only an incomplete f(α) spectrum (usually the top portion), we show that this can be overcome by an algorithmic approach, which is automated to compute the D_q and f(α) spectra from a time series for any embedding dimension. The scheme is first tested with the logistic attractor with known f(α) curve and subsequently applied to higher-dimensional cases. We also show that the scheme can be effectively adapted for analyzing practical time series involving noise, with examples from two widely different real world systems. Moreover, some preliminary results indicating that the set of four independent parameters may be used as diagnostic measures are also included.