In this paper, we put forward a hybrid methodology for combining forecasts to (stochastic) time series referred to as Wavelet Linear Combination (WLC) SARIMA-RNA with Multiple Stages. Firstly, the wavelet decomposition of level p is performed, generating (approximations of the) p+1 wavelet components (WCs). Then, the WCs are individually modeled by means of a Box and Jenkins’ model and an artificial neural network - in order to capture, respectively, plausible linear and non-linear structures of autodependence - for, then, being linearly combined, providing hybrid forecasts for each one. Finally, all of them are linearly combined by the WLC of forecasts (to be defined). For evaluating it, we used the Box
and Jenkins’ (BJ) models, artificial neural networks (ANN), and its traditional Linear Combination (LC1) of forecasts; and ANN integrated with the wavelet decomposition (ANNWAVELET), BJ model integrated with the wavelet decomposition (BJ-WAVELET), and its conventional Linear Combination (LC2) of forecasts. All predictive methods applied to the monthly time series of average flow of tributaries of the Itaipu Dam dam, located in Foz do Iguaçu, Brazil. In all analysis, the proposed hybrid methodology has provided higher predictive performance than the other ones
