数学理论与应用 ›› 2021, Vol. 41 ›› Issue (2): 109-.

• • 上一篇    

基于干预模型的上海市入境旅游统计分析

朱恩文   曹峻   朱安麒   张梅   

  1. 长沙理工大学 数学与统计学院,长沙, 410114
  • 出版日期:2021-06-30 发布日期:2021-08-18
  • 基金资助:
    长沙理工大学研究生创新项目(编号:CX2020SS86,CX2020SS87)

Statistical Analysis of Inbound Tourism in Shanghai Based on Intervention Model

  1. Changsha University of Science and Technology, School of Mathematics and Statistics, Changsha 410114, China
  • Online:2021-06-30 Published:2021-08-18

摘要: 本文选取2004年1月至2012年8月上海市入境游客人数月度数据, 在一般时间序列预测方法的基础上, 引入干预分析, 利用R软件对该时间序列进行预测.首先, 通过分析发现, 序列存在趋势效应和季节效应, 于是采用乘积季节模型拟合该时间序列, 并利用拟合的模型对接下来8个月的上海入境旅游人数进行预测; 其次, 将原始数据进行预处理后, 确定世博会产生影响的时间节点, 并将该时间序列按此节点分为两部分, 然后采用干预分析的方法, 建立干预组合模型, 再运用建立的干预模型预测接下来8个月的入境旅游人数; 最后, 将两个模型的预测结果进行比较, 计算各自的相对误差.通过比较发现, 干预模型的预测效果更好, 说明在存在突发事件或者是重大政策影响的情况下, 采用干预模型对时间序列进行分析和预测效果会更好.

关键词: 预测 ,  入境旅游 ,  , 时间序列 ,  , 干预分析

Abstract: This paper selects the monthly data of the number of visitors to Shanghai from January 2004 to August 2012. Based on the general time series prediction method, intervention analysis is introduced, and the R software is used to predict the time series. First of all, through analysis, the trend effect and seasonal effect of the series are found, so a product season model is used to fit the time series and predict the the number of visitors to Shanghai for the next 8 months. Secondly, after pre-processing the original data and determining the time point of the Expo impact, the time series is divided into two parts, by the time point then the method of intervention analysis is used to establish an intervention combination model to predict the number of visitors to Shanghai for the next 8 months. Finally, the prediction results of the two models are compared by calculating their relative errors. By comparison, it is found that the prediction effect of the intervention model is better, indicating that in the presence of emergencies or major policies, it is better to use an intervention model to analyze and predict the time series.

Key words: Forecast ,  Inbound tourist ,  Time series ,  , Intervention model