Time Series which can be arranged in levels according to, for example, the type of product, geography, consumption class, among others, are called Hierarchical Time Series (or grouped, if they have more than one aggregation variable). Information relating these series prediction is fundamental for decision-making at the management or operational level of all types of business. To meet these information, hierarchical forecasting techniques are used, which are focused on reducing costs and improving the accuracy of prediction. The objective of this work is to study approaches to aggregate / disaggregate predictions for Hierarchical or Grouped Time Series. As a result of the work there is the presentation of the approaches that represent the state of the art hierarchical forecast: Optimal Reconciliation approach (also called the Optimal Combination) and Top-Down Forecast Proportions approach. Still on the results highlight the analysis of the various hierarchical forecasting techniques found in the literature applied to two classic series of the Brazilian context: a grouped series of electricity consumption aggregated by region of the country and consumer class, and the hierarchical series air transport demand represented by the variable RPK (Revenue Passenger Kilometers). The predictive performance of the approaches was evaluated based on the metric MAPE and the Diebold-Mariano test was used to verify that the difference in performance of new and traditional approaches is significant.