Forecasting the air transport demand for passengers with neural modelling

Abstract

The air transport industry firmly relies on forecasting methods for supporting management decisions. However, optimistic forecasting has resulted in serious problems to the Brazilian industry in the past years. In this paper, models based on artificial neural networks are developed for the air transport passenger demand forecasting. It is found that neural processing can outperform the traditional econometric approach used in this field and can accurately generalise the learning time series behaviour, even in practical conditions, where a small number of data points is available. Feeding the input nodes of the neural estimator with pre-processed data, the forecasting error is evaluated to be smaller than 0.6%.