The GAS models (generalized autoregressive score) are time series models with time-varying parameters, which have their update mechanism drived by the scaled score of the likelihood function. The likelihood evaluation in these models is quite simple, as well as the incorporation of effects like asymmetry, long memory and other dynamics. Because they are based in the scaled score of the likelihood, it exploits the full structure of the predictive distribution to the update mechanism of the parameters, and not just mean or higher order moments. These characteristics, coupled with the ability to handle with multivariate and non-stationary processes, make the studied class a new alternative to the construction of models with time-varying parameters, particularly for non-Gaussian time series. In this dissertation, univariate GAS models were developed to analyze monthly series of streamflow of Paraibuna river (MG) and of capacity factor of a wind farm undisclosed in Northeast, using the gamma and beta distributions, respectively. In addition, a new bivariate GAS model with gamma and beta marginals was derived for the joint modeling of the streamflow and wind processes, in order to explore the seasonal complementarity between the series.