“The past two years have been extremelydifficull for the travel and tourism industry-notleast for the hospitality sector,”Alain-PhilippeFeutre.IH&RA CEO told delegates from some40 countries meeting in Cairo for ...“The past two years have been extremelydifficull for the travel and tourism industry-notleast for the hospitality sector,”Alain-PhilippeFeutre.IH&RA CEO told delegates from some40 countries meeting in Cairo for the 40thAnnual Congress of the Internationa J Hotel ＆Restaurant Association(5-9 December 2003).Yet three days of presentations and discussionsin the Egyptian capital reflected a newly foundconfidence among hotel operators,suppliers,destinations and other tourism experts as to theimminent revivaI of travel and tourism demand.展开更多
Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This pap...Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.展开更多
While climate is an important factor attracting tourists to certain destinations,it can also motivate people residing in a country with a harsh climate to move to another location.By applying X-12 decompositions and a...While climate is an important factor attracting tourists to certain destinations,it can also motivate people residing in a country with a harsh climate to move to another location.By applying X-12 decompositions and a panel data regression analysis,this study analyzes the pull and push effects of climatic seasonal factors between destination(Hainan Island,China) and source countries(Russia and South Korea).The findings show that climatic seasonal factors have significant pulling and pushing effects on seasonal patterns of tourism demand,with temperature being the main factor.Furthermore,the number of paid vacation days in the source country affects that country's sensitivity to climatic seasonal factors;countries with a higher numbers of paid vacation days are more sensitive to climatic conditions.Lastly,future global warming may causes the aforementioned pull and push effects to abate,which will have an unavoidable influence on tourism industries.展开更多
文摘“The past two years have been extremelydifficull for the travel and tourism industry-notleast for the hospitality sector,”Alain-PhilippeFeutre.IH&RA CEO told delegates from some40 countries meeting in Cairo for the 40thAnnual Congress of the Internationa J Hotel ＆Restaurant Association(5-9 December 2003).Yet three days of presentations and discussionsin the Egyptian capital reflected a newly foundconfidence among hotel operators,suppliers,destinations and other tourism experts as to theimminent revivaI of travel and tourism demand.
文摘Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.
基金Under the auspices of the National Natural Science Foundation of China(No.41430528,41671036)Ministry of Education of Humanities,Social Science Project(No.16YJC790060)+2 种基金Social Science Planning Annual Project of Sichuan,China(No.SC15B046)Soft Science Research Project of Sichuan,China(No.2015ZR0225)Fundamental Research Funds for the Central Universities(No.skqy201639)
文摘While climate is an important factor attracting tourists to certain destinations,it can also motivate people residing in a country with a harsh climate to move to another location.By applying X-12 decompositions and a panel data regression analysis,this study analyzes the pull and push effects of climatic seasonal factors between destination(Hainan Island,China) and source countries(Russia and South Korea).The findings show that climatic seasonal factors have significant pulling and pushing effects on seasonal patterns of tourism demand,with temperature being the main factor.Furthermore,the number of paid vacation days in the source country affects that country's sensitivity to climatic seasonal factors;countries with a higher numbers of paid vacation days are more sensitive to climatic conditions.Lastly,future global warming may causes the aforementioned pull and push effects to abate,which will have an unavoidable influence on tourism industries.
