The socalled 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 welldefined 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 socalled shuffled one, R, an empirical function for the Hurst exponent versus R has been established. Finally to make the concept more obvious, we applied this approach to some financial series
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