基于干预SARIMA模型对疫情后民航客运量的预测  

Prediction of Civil Aviation Passenger Traffic after the Epidemic Based on the Intervention SARIMA Model

作　　者：廖俊林 

机构地区：[1]华南师范大学数学科学学院,广东 广州

出　　处：《统计学与应用》2023年第4期1020-1033,共14页Statistical and Application

摘　　要：民航客运量不仅是交通运输部门确定合理交通设施规模的基础,同时也是保障机场设施高效率利用的前提。因此,对疫情后民航客运量展开科学预测显得尤为重要。针对现阶段对客运量预测中未能定量考虑到新冠疫情对客运量影响的研究缺口,本文在建立SARIMA(1,1,1) × (0,1,1)12模型对未发生疫情下我国民航客运量展开预测的基础上,运用干预分析方法定量衡量新冠疫情对民航客运量的影响,进而对疫情后民航客运量展开预测。结果表明,相比于单一SARIMA模型,干预SARIMA模型对疫情后民航客运量短期预测效果表现良好,后续可采用类似干预分析方法将经济状况、政策变化、航空公司策略等事件考虑进来,以更全面分析和预测民航客运量的变化。Civil aviation passenger traffic is not only the basis for the transportation sector to determine the reasonable scale of transportation facilities, but also a prerequisite to ensure the efficient use of airport facilities. Therefore, it is particularly important to make scientific forecasts of civil aviation passenger traffic after the epidemic. Based on the SARIMA(1,1,1) × (0,1,1)12 model, this paper uses intervention analysis to quantitatively measure the impact of the new epidemic on China’s civil aviation passenger traffic, and then forecast the post-epidemic civil aviation passenger traffic, in order to address the gap that the current passenger traffic forecast does not quantitatively take into account the impact of the new epidemic. The results show that the intervention SARIMA model is effective in predicting passenger traffic in China after the epidemic. The results show that compared with the single SARIMA model, the intervention SARIMA model performs well in the short-term prediction of civil aviation passenger traffic after the epidemic, and the intervention analysis method can be similarly used to take into account the economic situation, policy changes, airline strategies and other events in order to analyze and predict the changes in civil aviation passenger traffic more comprehensively.

关 键 词：民航客运量预测 疫情干预分析 SARIMA模型 

分 类 号：F56[经济管理—产业经济]