Estimation of the spectral exponent of 1/ ƒγ process corrupted by white noise

1/ f^γ noise is used to model a large number of processes; such as network traffic data, GPS (Global Positioning System) noise, financial and biological data. However, observations on real data have shown that assumption of a purely 1/ f^γmodel may be inadequate, as the measured data may contain trend, periodicity or noise. These are considerable factors effecting the estimation of γ. In this work, we examine real data from GPS noise and network traffic data and apply a wavelet based method for the removal of the effect of white noise in these data sets.