基于乘积季节模型的铁路客运量预测

The Forecast of Railway Passenger Traffic Volume Based on Multiple Seasonal ARIMA

利用中国铁路客运量2005-2016年的月度数据资料,采用乘积季节模型进行建模,对2017年1-6月进行预测;在Eviews和R软件操作下训练与测试数据,分别得到两种乘积季节模型;结果表明:两种软件下客运量的预测误差率均控制在10%以内,两种模型都能较好地预测铁路客运量未来数据的变化情况;通过比较,Eviews建立乘积季节模型,数据分析思维更加严谨,但操作较为复杂,平均预测误差率为4.59%,预测正确率稍低;R软件利用程辑包中相关分析、参数估计与预测函数等,可直接进行分析与预测,操作较为简便,平均预测误差率为3.36%,数据预测正确率较高;通过利用R软件建立ARIMA(2,1,1)×(1,1,1)12模型,此时模型预测精度较好,为预测未来全国铁路客运量变化提供一定的参考价值。

Using the monthly data of China railway passenger traffic volume from 2005 to 2016,we build the multiple seasonal ARIMA model and forecast from January to June of 2017. Through the analysis of Eviews and R software,two models are obtained. The results show that：（1） The prediction error rate under the two types of statistical software is less than 10%,both of them can predict the future value of railway passenger traffic well.（2） Establishing the multiple seasonal ARIMA under the analysis of Eviews software,the analysis thinking is more rigorous and the operation is more complicated,the average of forecast error rate is 4. 59% and it has low accuracy; Using R to analyze and model,we can directly use the function about correlation,parameter estimation and prediction in the package to analyze and prodict,the average of forecast error rate is 3.36%,the operation is more simple and the accuracy of prediction is more high. Through this research,ARIMA（2,1,1） ×（1,1,1）12 model can be established by using R software. The prediction accuracy of the model is better,which provides some reference value for predicting the future change of railway passenger traffic volume in China.