  The so-called level crossing analysis has been used to investigate the
empirical data set. But there is a lack of interpretation for what is reflected
by the level crossing results. The fractional Gaussian noise as a well-defined
stochastic series could be a suitable benchmark to make the level crossing
findings more sense. In this article, we calculated the average frequency of
upcrossing for a wide range of fractional Gaussian noises from logarithmic
(zero Hurst exponent, H=0), to Gaussian, H=1, ($0<H<1$). By introducing the
relative change of the total numbers of upcrossings for original data with
respect to so-called shuffled one, $\mathcal{R}$, an empirical function for the
Hurst exponent versus $\mathcal{R}$ has been established. Finally to make the
concept more obvious, we applied this approach to some financial series.
