Empleando modelos jerárquicos para encontrar el mejor modelo para pronosticar los galones de gasolina corriente demandados en Bogotá (Colombia)

The objective of this analysis is to find the best hierarchical model to forecast the total demand for regular gasoline in Bogotá, Colombia and, therefore, the collection of gasoline surcharges, which is an important tax used to finance road networks and massive transportation systems. We used data reported by 6 wholesalers of regular gasoline in the city, and used two univariate approaches (ARIMA and exponential smoothing (ETS)), five methods and different minimization algorithms to forecast gallons of regular gasoline. Results show that the best combination of these parameters is an ETS model under a simple univariate forecast.