This research focuses on the development of hybrid approaches that combine ensemble-based supervised machine learning techniques and time series methods to obtain accurate forecasts for a wide range of variables and processes. It also includes the development of smart selection heuristics, i.e., procedures that can select, among the pool of forecasts originated via ensemble methods, those with the greatest potential of delivering accurate forecasts after aggregation. Such combinatorial approaches allow the forecasting practitioner to deal with different stylized facts that may be present in time series, such as nonlinearities, stochastic components, heteroscedasticity, structural breaks, among others, and deliver satisfactory forecasting results, outperforming benchmarks on many occasions. The thesis is divided into a series of essays. The first endeavor proposed an alternative method to generate ensemble forecasts which delivered satisfactory forecasting results for certain types of electricity consumption time series. In a second effort, a novel forecasting approach combining Bootstrap aggregating (Bagging) algorithms, time series methods and regularization techniques was introduced to obtain accurate forecasts of natural gas consumption and energy supplied series across different countries. A new variant of Bagging, in which the set of classifiers is built by means of a Maximum Entropy Bootstrap routine, was also put forth. The third contribution brought a series of innovations to model selection and model combination in forecasting routines. Gains in accuracy for both point forecasts and prediction intervals were demonstrated by means of an extensive empirical experiment conducted on a wide range of series from the M- Competitions.