  We introduce a new method for detecting scaling in time series. The method
uses the properties of the probability flux for stochastic self-affine
processes and is called the probability flux analysis (PFA). The advantages of
this method are: 1) it is independent of the finiteness of the moments of the
self-affine process; 2) it does not require a binning procedure for numerical
evaluation of the the probability density function. These properties make the
method particularly efficient for heavy tailed distributions in which the
variance is not finite, for example, in Levy alpha-stable processes. This
utility is established using a comparison with the diffusion entropy (DE)
method.
