中文摘要：

针对机场货运量预测不能满足机场实际运行精度等缺点，提出一种季节性ARIMA和RBF神经网络集成模型预测机场货运量，该模型首先利用季节性ARIMA模型预测机场货运量线性部分，然后用RBF神经网络模型预测机场货运量非线性部分．最后把非线性部分预测结果作为线性部分预测结果的补偿．得到最终预测结果。实验结果表明，新模型可以有效结合季节性ARIMA和RBF神经网络各自的优点；相对单一季节性ARIMA模型和单一RBF神经网络模型预测精度分别提高了6．30％和3．32％，预测精度满足机场实际运行要求。

英文摘要：

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 SARIMA- RBF, the first use of seasonal ARIMA is to forecast the linear part of airport cargo, and then to forecast the non- linear 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.