基于乘法ARIMA模型的公路客运量预测

在收集了2004-2007年我国公路客运量月度数据的基础上,进行时间序列分析,建立了我国公路客运量月度数据的求和自回归移动平均（ARIMA）模型。分析结果显示：与常用的多项式曲线预测模型和灰色预测模型相比,模型ARIMA（2,1,2）（1,1,1）12有更好的预测效果,可以用于我国公路客运量月度数据的短期预测。

Forecast for Highway Passenger Carrying Capacity Based on Multiple ARIMA Model

Based on the collection of the monthly data of China＇s highway passenger carrying capacity from 2004 to 2007, established the Multiple ARIMA Model about monthly data of China＇s highway passenger carrying capacity by analysis of time series. The result showed that compared with Polynomial Curve Prediction Model and Grey Forecasting Model, the model ARIMA （2,1,2）（1,1,1）^12 had a better predictive effect and can be used for short-term forecast of the monthly data of China＇s highway passenger carrying capacity.