Abstract:

The model of integrated seasonal ARIMA and RBF neural network (SARIMA-RBF) is proposed to solve the problem that airport cargo forecasting accuracy can not meet the actual operation of the airport. In the SARIMARBF,the first use of seasonal ARIMA is to forecast the linear part of airport cargo, and then to forecast the nonlinear part of airport cargo with RBF neural network, finally the nonlinear forecasting result is taken as the compensation of linear forecasting result to get the final forecasting result. Experimental results show that the new model can be combined with respective advantages of seasonal ARIMA and RBF neural network. The new model compared with single seasonal ARIMA model and single RBF neural network model forecasting accuracy are improved by 6.30% and 3.32%; and its forecasting accuracy can meet the actual operation of the airport.

Key words: airport cargo, SARIMA, RBF neural network, integrate, forecasting