Hierarchical forecasting of air traffic demand in Brazil: Evidence from forecast reconciliation

This study presents a hierarchical forecasting framework for air traffic in Brazil, leveraging data provided by the National Civil Aviation Agency (ANAC). The framework integrates hierarchical representations of Brazilian air traffic with base forecasting models, such as ETS and SARIMA, and employs classical, as well as state-of-the-art optimal reconciliation techniques to enhance the accuracy of air traffic demand forecasts. Our results demonstrate that forecast reconciliation strategies can substantially improve the reliability of air traffic demand forecasts, thereby supporting more effective resource allocation, infrastructure planning, and tourism management. Our findings further show that classical reconciliation strategies, specifically bottom-up and top-down approaches – in case of the latter particularly Gross–Sohl Method F (TDGSF) – consistently provide strong performance across hierarchical levels. Rather than favoring more complex methods, the results emphasize the importance of aligning reconciliation strategies with data structure and volatility. Finally, our findings highlight the potential of hierarchical forecasting to capture the complex dynamics of Brazil’s air travel market, leading to better decision-making and strategic planning.