Improving the Forecasting Accuracy of Civil Aviation Passengers Based on Machine Learning Models

摘要：

　　The paper is to predict the number of daily passengers in an airline company for the airline from Beijing to Sanya,using the historic data from 2010 to 2016.The forecasting is conducted out by means of machine learning,such as the multi-variable regression model,the support vector regression model,the ARMA-improved model,and the RBF-based neural network model.Upon verification,the average absolute error of the said four models is 5.27%,7.61%,5.07%and 3.34%respectively.They can be applied to improve the forecasting of passenger number because of their high forecasting precision.

关键词： multi-variable regression support vector regression ARMA-improved model neural network

作者: Xia Liu Xia Huang Lei Chen Zhao Qiu Mingrui Chen

作者单位: Department of Public Course Teaching Sanya Aviation and Tourism College Sanya,China College of Information Science & Technology Hainan University Haikou,China

会议类型: 国际会议

会议名称: 第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

会议地点: 南京

会议语种:英文

页码: 298-304

在线出版日期: 2017-10-12（万方平台首次上网日期，不代表论文的发表时间）