基于SARIMA模型的铁路月度客运量预测

Monthly Railway Passenger Traffic Volume Forecasting Based on SARIMA Model

铁路月度客运量数据序列在长期内具有线性增长趋势,且在短期内又随月份波动变化明显。本文通过构建季节差分移动自回归模型(SARIMA)对2016年铁路月度客运量进行精确预测,挖掘铁路月度客运量的季节性波动规律,为铁路客运管理人员调整列车运行图,制定客车开行方案提供重要参考,以便于铁路客运站确定客流高峰预警时间和提高客运组织效率。

The data sequence of monthly railway passenger traffic volume exhibits a trend of linear growth in the long term, but it fluctuates significantly with the month in the short term. This study uses the SARIMA model to accurately predict monthly railway passenger traffic volume for 2016 and determine the seasonal fluctuations in monthly traffic, which can provide an important reference for the railway department in adjusting train diagrams and planning passenger trains. It can also help railway terminal staff know passenger peak times in advance, and can improve the efficiency of railway passenger transport organizations.