摘要
The prediction of the Baidu index for tourism demand has been increasingly focused on by scholars.However,few studies have evaluated the predictive power of the Baidu index for hotel guest arrivals in fine granularity at the micro level.Taking Guilin as a case study,we use the OLS regression method to quantitatively investigate the forecasting power of the Baidu index for daily hotel guest arrivals and to comprehensively evaluate the performance of the forecasting model and to optimize the forecasting model by deeply mining the hidden characteristics of tourism flow in a special case study.The contributions of this papermainly have threefold:first,to the best of our knowledge,based on the actual full-example of daily hotel guest check-in data in fine granularity,we evaluated the predictive power of the Baidu index by comparison of 5 forecasting models for the first time.Second,we proposed two metrics for forecasting:the trend forecasting index and the forecasting stability index.Finally,we introduce a kind of punishment strategy to optimize forecasting models based on the potential pattern of research objects.
出处
《国际计算机前沿大会会议论文集》
2022年第2期185-211,共27页
International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）
基金
supported by the research on key technology of tourism destination safety warning and its application demonstration granted by No.Guike AB17195028
technology development of tourist safety warning system for smart scenic spot and virtual spatiotemporal reconstruction of special culture and its application demonstration granted by No.20170220
research on sustainable utility technology integration of Longji terrace landscape resources and tourism industry demonstration granted by No.20180102-2,Guangxi natural science fund by No.2018GXNSFAA138209.
作者简介
Yajun Jiang,jyjall@163.com。
