关键词: 民航货邮周转量, GM（1, 1）模型, 自回归移动平均（ARIMA）模型, 组合模型

Abstract: ARIMA model has a better fitting effect on seasonal feature. Grey model can accurately reflect the growth trend of time series. Combined the characteristics of civil aviation RFTK with the advantages of ARIMA model and GM（1, 1） model, comprehensively using the analysis method of time series, this paper presents the combination prediction model of ARIMA-GM. It respectively establishes the time series model of ARIMA model and GM（1, 1） model to show the dynamic rule of civil aviation RFTK changing as time passing. At last, for more accurately predicting the month civil aviation RFTK, this paper puts forward the forecasting method of combination model, and makes an exact forecast of the civil aviation RFTK in a few months. The combination model is a good solution to the practical problems for civil aviation RFTK forecasting, based on which it can have a macro-grasp of civil aviation RFTK market trend, which will certainly be conducive to economic decision-making.

Key words: civil aviation RFTK, GM（1, 1） model, Autoregressive Integrated Moving Average（ARIMA） model, combination model