Given the importance of web search volume for reflecting tourists'preferences for certain tourism services and destinations,incorporating these data into forecasting models can significantly improve forecasting pe...Given the importance of web search volume for reflecting tourists'preferences for certain tourism services and destinations,incorporating these data into forecasting models can significantly improve forecasting performance.This study enriches the literature on tourism demand forecasting and tourists'search behavior through segmented Baidu search volume data.First,this study divides Baidu search volume data based on volume sources and periods.Then,by analyzing the most relevant keywords in tourism demand in different segments,this study captures the dynamic characteristics of tourist search behavior.Finally,this study adopts a series of econometric and machine learning models to further improve the performance of tourism demand and forecasting.The findings indicate that tourists’search behavior has changed significantly with the prevalence and popularization of 4G technology and suggest that search volume improves forecasting performance,especially search volume on mobile terminals,from 2014M1–2019M12.展开更多
利用皮尔森相关系数法处理网络搜索数据,用灰狼算法(grey wolf optimizer,GWO)优化支持向量回归(support vector regression,SVR)中的参数,提出并实现一种基于网络搜索数据和GWO-SVR模型的旅游短期客流量预测模型,并用参数优化后的SVR...利用皮尔森相关系数法处理网络搜索数据,用灰狼算法(grey wolf optimizer,GWO)优化支持向量回归(support vector regression,SVR)中的参数,提出并实现一种基于网络搜索数据和GWO-SVR模型的旅游短期客流量预测模型,并用参数优化后的SVR对客流量进行建模预测.以四川省九寨沟和四姑娘山两个景区为例,构建GWO-SVR、ARIMA、BPNN、SVR、CS-SVR、PSO-SVR和无网络搜索数据等客流量预测模型进行实证分析.结果表明,GWO-SVR模型均优于其他模型,具有更高的预测精度.展开更多
基金partly supported by the National Natural Science Foundation of China under Grant No.72101197by the Fundamental Research Funds for the Central Universities under Grant No.SK2021007.
文摘Given the importance of web search volume for reflecting tourists'preferences for certain tourism services and destinations,incorporating these data into forecasting models can significantly improve forecasting performance.This study enriches the literature on tourism demand forecasting and tourists'search behavior through segmented Baidu search volume data.First,this study divides Baidu search volume data based on volume sources and periods.Then,by analyzing the most relevant keywords in tourism demand in different segments,this study captures the dynamic characteristics of tourist search behavior.Finally,this study adopts a series of econometric and machine learning models to further improve the performance of tourism demand and forecasting.The findings indicate that tourists’search behavior has changed significantly with the prevalence and popularization of 4G technology and suggest that search volume improves forecasting performance,especially search volume on mobile terminals,from 2014M1–2019M12.
文摘利用皮尔森相关系数法处理网络搜索数据,用灰狼算法(grey wolf optimizer,GWO)优化支持向量回归(support vector regression,SVR)中的参数,提出并实现一种基于网络搜索数据和GWO-SVR模型的旅游短期客流量预测模型,并用参数优化后的SVR对客流量进行建模预测.以四川省九寨沟和四姑娘山两个景区为例,构建GWO-SVR、ARIMA、BPNN、SVR、CS-SVR、PSO-SVR和无网络搜索数据等客流量预测模型进行实证分析.结果表明,GWO-SVR模型均优于其他模型,具有更高的预测精度.
