Hierarchical forecasting methods exploit time series data structure, often producing more accurate and reliable forecasts through reconciliation strategies. This study introduces a hierarchical framework for forecasting air traffic arrivals at Brazilian airports, using data from 2000 to 2019 provided by the National Civil Aviation Agency (ANAC). We apply and compare ARIMA and ETS forecasting methods, along with various hierarchical reconciliation techniques, to improve forecast accuracy across different aggregation levels. By benchmarking our methods against traditional and state-of-the-art approaches, we aim to address a significant gap in the modeling and forecasting of tourism flows in Brazil. The findings provide valuable insights for businesses and policymakers, offering a robust tool for anticipating air traffic demand trends. Our results underscore the potential of hierarchical forecasting to capture the dynamics of Brazil s air travel market, helping optimize resource allocation, infrastructure planning, and tourism management.