  We study the scaling behavior of the fluctuations, as extracted through
wavelet coefficients based on discrete wavelets. The analysis is carried out on
a variety of physical data sets, as well as Gaussian white noise and binomial
multi-fractal model time series and the results are compared with continuous
wavelet based average wavelet coefficient method. It is found that high-pass
coefficients of wavelets, belonging to the Daubechies family are quite good in
estimating the true power in the fluctuations in a non-stationary time series.
Hence, the fluctuation functions based on discrete wavelet coefficients find
the Hurst scaling exponents accurately.
