The paper proposes a wavelet-based forecasting method for time series. We used the multiresolution decomposition of the signal implemented using trous wavelet transform. We combined the Stationary Wavelet Transform (SWT) with four prediction methodologies : Artificial Neural Networks, ARIMA, Linear regression and Random walk. These techniques were applied to two types of real data series: WiMAX network traffic and financial. We proved that the best results are obtained using ANN combined with the wavelet transform.Also, we compared the results using various types of mother wavelets. It is shown that Haar and Reverse biorthogonal 1 give the best results.