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We propose a wavelet based method for the characterization of the scalingbehavior of non-stationary time series. It makes use of the built-in ability ofthe wavelets for capturing the trends in a data set, in variable window sizes.Discrete wavelets from the Daubechies family are used to illustrate theefficacy of this procedure. After studying binomial multifractal time serieswith the present and earlier approaches of detrending for comparison, weanalyze the time series of averaged spin density in the 2D Ising model at thecritical temperature, along with several experimental data sets possessingmulti-fractal behavior.Comment: 4 pages, 4 figures. Accepted for publication in PR

Wavelet analysis and scaling properties of time series