FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W645005145> ?p ?o ?g. }

• W645005145 endingPage "82" @default.
• W645005145 startingPage "75" @default.
• W645005145 abstract "Forecasting tourism demand plays an important role in formulating national tourism development policy and strategic planning.It is also important in optimizing the allocation of tourism market resources and drawing up strategic plans and decision making for tourism businesses.As a result,forecasting tourist arrivals has been a main focus in tourism research.However,making such forecasts is a difficult task for tourism researchers.This is particularly the case with widely fluctuating numbers of tourist arrivals in scenic areas,which are impacted by seasonality,external shocks and economic cycles.Making such medium-and long-term forecasts is one of the most challenging areas in tourism research. In this paper,the widely used method of least squares support vector machines was applied in forecasting two-year tourist arrivals in Huangshan scenic areas using monthly data(from January 1987 to December 2010).The analysis results showed that forecast performance indexes using least squares support vector machine algorithms are better than with the BP neural network,ARIMA forecasting model,and combined forecasting using empirical mode decomposition and least squares support vector machines.Moreover,least squares support vector machine algorithms are superior in both prediction accuracy and computing speed.The ability of least squares support vector machines to forecast tourist arrivals has high practical value and can provide a scientific basis for planning and strategy management of scenic areas. In addition,nine forecasting models were assessed: the time-series regression prediction model,the Holt-Winters addition and multiplication model,the TRAMO/SEATS model,a combination of the wavelet and ARIMA forecasting models,the wavelet neural network prediction method,and the four methods cited in the previous paragraph.We investigated the forecasting ability of these nine models in predicting tourist arrivals over one-and two-year time scales in Huangshan scenic areas.Owing to restrictions of space,however,we are unable to detail all the analysis procedures in this paper.The results showed that least squares support vector machines was superior in performance.It was better than the other single forecasting methods and also exceeded the combination of the wavelet and ARIMA forecasting models,the wavelet neural network,and the combined empirical mode decomposition and least squares support vector machines over the two-year time scale;however,these advantages were not evident over the one-year time scale.The effectiveness of least squares support vector machines in forecasting other tourist arrival data requires further examination. The main difficulties in forecasting monthly tourist arrival data lie in the large fluctuations in the numbers of tourists and the lack of data relating to tourist arrivals.These data include such economic figures as GDP,quarterly unit statistics,tourist arrival data other than annual statistics of inbound tourists,the vulnerability to external shocks,and diversity in the data from different regions.In all,the diversity,complexity,and fluctuation in different types of tourist arrivals make it difficult to find a satisfactory tourist arrival forecasting method that is universally accurate.Thus,greater efforts need to be made to develop better methods for accurately forecasting tourist arrival data for different scenic areas." @default.
• W645005145 created "2016-06-24" @default.
• W645005145 creator A5039701399 @default.
• W645005145 creator A5049006783 @default.
• W645005145 creator A5050893023 @default.
• W645005145 creator A5055275568 @default.
• W645005145 date "2013-01-01" @default.
• W645005145 modified "2023-09-28" @default.
• W645005145 title "Research on Medium-term Prediction of Tourist Arrivals in Scenic Areas Based on Least Squares Support Vector Machines" @default.
• W645005145 hasPublicationYear "2013" @default.
• W645005145 type Work @default.
• W645005145 sameAs 645005145 @default.
• W645005145 citedByCount "0" @default.
• W645005145 crossrefType "journal-article" @default.
• W645005145 hasAuthorship W645005145A5039701399 @default.
• W645005145 hasAuthorship W645005145A5049006783 @default.
• W645005145 hasAuthorship W645005145A5050893023 @default.
• W645005145 hasAuthorship W645005145A5055275568 @default.
• W645005145 hasConcept C105795698 @default.
• W645005145 hasConcept C119857082 @default.
• W645005145 hasConcept C121332964 @default.
• W645005145 hasConcept C12267149 @default.
• W645005145 hasConcept C145828037 @default.
• W645005145 hasConcept C149782125 @default.
• W645005145 hasConcept C151406439 @default.
• W645005145 hasConcept C154945302 @default.
• W645005145 hasConcept C162324750 @default.
• W645005145 hasConcept C166957645 @default.
• W645005145 hasConcept C185429906 @default.
• W645005145 hasConcept C18918823 @default.
• W645005145 hasConcept C205649164 @default.
• W645005145 hasConcept C24338571 @default.
• W645005145 hasConcept C33923547 @default.
• W645005145 hasConcept C41008148 @default.
• W645005145 hasConcept C42475967 @default.
• W645005145 hasConcept C61797465 @default.
• W645005145 hasConcept C62520636 @default.
• W645005145 hasConcept C9936470 @default.
• W645005145 hasConceptScore W645005145C105795698 @default.
• W645005145 hasConceptScore W645005145C119857082 @default.
• W645005145 hasConceptScore W645005145C121332964 @default.
• W645005145 hasConceptScore W645005145C12267149 @default.
• W645005145 hasConceptScore W645005145C145828037 @default.
• W645005145 hasConceptScore W645005145C149782125 @default.
• W645005145 hasConceptScore W645005145C151406439 @default.
• W645005145 hasConceptScore W645005145C154945302 @default.
• W645005145 hasConceptScore W645005145C162324750 @default.
• W645005145 hasConceptScore W645005145C166957645 @default.
• W645005145 hasConceptScore W645005145C185429906 @default.
• W645005145 hasConceptScore W645005145C18918823 @default.
• W645005145 hasConceptScore W645005145C205649164 @default.
• W645005145 hasConceptScore W645005145C24338571 @default.
• W645005145 hasConceptScore W645005145C33923547 @default.
• W645005145 hasConceptScore W645005145C41008148 @default.
• W645005145 hasConceptScore W645005145C42475967 @default.
• W645005145 hasConceptScore W645005145C61797465 @default.
• W645005145 hasConceptScore W645005145C62520636 @default.
• W645005145 hasConceptScore W645005145C9936470 @default.
• W645005145 hasIssue "4" @default.
• W645005145 hasLocation W6450051451 @default.
• W645005145 hasOpenAccess W645005145 @default.
• W645005145 hasPrimaryLocation W6450051451 @default.
• W645005145 hasRelatedWork W2015122449 @default.
• W645005145 hasRelatedWork W2039802810 @default.
• W645005145 hasRelatedWork W2067284569 @default.
• W645005145 hasRelatedWork W2082470815 @default.
• W645005145 hasRelatedWork W2116427954 @default.
• W645005145 hasRelatedWork W2117055950 @default.
• W645005145 hasRelatedWork W2166505250 @default.
• W645005145 hasRelatedWork W2245678901 @default.
• W645005145 hasRelatedWork W2347085743 @default.
• W645005145 hasRelatedWork W2392314948 @default.
• W645005145 hasRelatedWork W2568246222 @default.
• W645005145 hasRelatedWork W2585667993 @default.
• W645005145 hasRelatedWork W2591941015 @default.
• W645005145 hasRelatedWork W2794095758 @default.
• W645005145 hasRelatedWork W3003248340 @default.
• W645005145 hasRelatedWork W3009020934 @default.
• W645005145 hasRelatedWork W3042645226 @default.
• W645005145 hasRelatedWork W3121260070 @default.
• W645005145 hasRelatedWork W3195028713 @default.
• W645005145 hasRelatedWork W3196723177 @default.
• W645005145 hasVolume "28" @default.
• W645005145 isParatext "false" @default.
• W645005145 isRetracted "false" @default.
• W645005145 magId "645005145" @default.
• W645005145 workType "article" @default.