Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach

Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregr...

[1]  Shouyang Wang,et al.  Forecasting tourist arrivals with machine learning and internet search index , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[2]  B. Murray,et al.  A Model of Tourist Information Search Behavior , 1999 .

[3]  Haiyan Song,et al.  A meta-analysis of international tourism demand forecasting and implications for practice , 2014 .

[4]  Haiyan Song,et al.  Impacts of the Financial and Economic Crisis on Tourism in Asia , 2010 .

[5]  S. Divisekera,et al.  Economic Effects of Advertising on Tourism Demand: A Case Study , 2006 .

[6]  Tao Chen,et al.  Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index , 2018, Tourism Management.

[7]  Stefan Lessmann,et al.  Spurious patterns in Google Trends data - An analysis of the effects on tourism demand forecasting in Germany , 2019 .

[8]  Michelle Baddeley,et al.  Running regressions : a practical guide to quantitative research in economics, finance and development studies , 2009 .

[9]  Ling Li,et al.  Big data in tourism research: A literature review , 2018, Tourism Management.

[10]  Roberto Rivera,et al.  A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data , 2015, 1512.08097.

[11]  Maria Lexhagen,et al.  Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden , 2018, Information Technology & Tourism.

[12]  Ping-Feng Pai,et al.  Tourism demand forecasting using novel hybrid system , 2014, Expert Syst. Appl..

[13]  K. Malek,et al.  Forecasting casino revenue by incorporating Google trends , 2018 .

[14]  Ying Liu,et al.  A preprocessing method of internet search data for prediction improvement: application to Chinese stock market , 2012, DM-IKM '12.

[15]  Rob Law,et al.  A novel hybrid model for tourist volume forecasting incorporating search engine data , 2017 .

[16]  Fang-Mei Tseng,et al.  Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model , 2018 .

[17]  Boriss A. Siliverstovs,et al.  Google Trends and reality: Do the proportions match?: Appraising the informational value of online search behavior: Evidence from Swiss tourism regions , 2016 .

[18]  Rob Law,et al.  A Rough Set Approach To Hotel Expenditure Decision Rules Induction , 1998 .

[19]  Bing Pan,et al.  Online information search: vacation planning process. , 2006 .

[20]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[21]  Gang Li,et al.  Tourism Demand Modelling and Forecasting: How Should Demand Be Measured? , 2010 .

[22]  Sen Cheong Kon,et al.  Neural Network Forecasting of Tourism Demand , 2005 .

[23]  Kevin K. F. Wong,et al.  Modeling Seasonality in Tourism Forecasting , 2005 .

[24]  Zvi Schwartz,et al.  Forecasting Short Time-Series Tourism Demand with Artificial Intelligence Models , 2006 .

[25]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[26]  V. Cho A comparison of three different approaches to tourist arrival forecasting , 2003 .

[27]  Christine Vogt,et al.  Twenty-Five Years Past Vogt: Assessing the Changing Information Needs of American Travellers , 2017, ENTER.

[28]  Donyaprueth Krairit,et al.  Tourists' external information search behavior model: the case of Thailand , 2011 .

[29]  Rob Law,et al.  Forecasting Tourism Demand with Decomposed Search Cycles , 2020, Journal of Travel Research.

[30]  Yongjun Sung,et al.  Predicting selfie-posting behavior on social networking sites: An extension of theory of planned behavior , 2016, Comput. Hum. Behav..

[31]  Noratikah Abu,et al.  Tourism demand forecasting – a review on the variables and models , 2019, Journal of Physics: Conference Series.

[32]  C. Witt,et al.  Forecasting tourism demand: A review of empirical research , 1995 .

[33]  V. Cho Tourism Forecasting and its Relationship with Leading Economic Indicators , 2001 .

[34]  Egon Smeral,et al.  The Impact of the Financial and Economic Crisis on European Tourism , 2009 .

[35]  Stephen F. Witt,et al.  Factors Influencing Demand for International Tourism: Tourism Demand Analysis Using Structural Equation Modelling, Revisited , 2001 .

[36]  Wei-Chiang Hong,et al.  SVR with hybrid chaotic genetic algorithms for tourism demand forecasting , 2011, Appl. Soft Comput..

[37]  José Juan Cáceres-Hernández,et al.  Forecasting tourists’ characteristics by a genetic algorithm with a transition matrix , 2007 .

[38]  George Athanasopoulos,et al.  Bagging in Tourism Demand Modeling and Forecasting , 2018 .

[39]  Chris Birchenhall,et al.  Seasonality and the Order of Integration for Consumption , 2009 .

[40]  Hal R. Varian,et al.  Big Data: New Tricks for Econometrics , 2014 .

[41]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[42]  C. Artola,et al.  Can internet searches forecast tourism inflows , 2015 .

[43]  Kamran Shahanaghi,et al.  Tourist arrival forecasting by evolutionary fuzzy systems. , 2011 .

[44]  Mike G. Tsionas,et al.  Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model , 2019 .

[45]  Emmanuel Sirimal Silva,et al.  Forecasting tourism demand with denoised neural networks , 2019, Annals of Tourism Research.

[46]  Ana María Munar,et al.  Tourist information search and destination choice in a digital age , 2012 .

[47]  Chulmo Koo,et al.  The influence of tourism website on tourists' behavior to determine destination selection: : A case study of creative economy in Korea , 2015 .

[48]  L. Grassini,et al.  Foreign arrivals nowcasting in Italy with Google Trends data , 2018 .

[49]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[50]  Chang Jui Lin,et al.  Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan , 2011 .

[51]  Stefan Gindl,et al.  Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria , 2018, Tourism Economics.

[52]  C. Grönroos Service logic revisited: who creates value? And who co‐creates? , 2008 .

[53]  Ruey-Chyn Tsaur,et al.  The adaptive fuzzy time series model with an application to Taiwan's tourism demand , 2011, Expert Syst. Appl..

[54]  Prosper F. Bangwayo-Skeete,et al.  Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach , 2015 .

[55]  Rob Law,et al.  Forecasting tourism demand with composite search index , 2017 .

[56]  Maria Lexhagen,et al.  Topic Detection: Identifying Relevant Topics in Tourism Reviews , 2016, ENTER.

[57]  Yan Carrière-Swallow,et al.  Nowcasting With Google Trends in an Emerging Market , 2013 .

[58]  D. Fesenmaier,et al.  Expanding the functional information search model , 1998 .

[59]  S. Smith,et al.  Tourism policy and planning. , 2020, Tourism.

[60]  Albert Sesé,et al.  Designing an artificial neural network for forecasting tourism time series , 2006 .

[61]  Maximo Camacho,et al.  Forecasting travellers in Spain with Google’s search volume indices , 2018 .

[62]  İclal Çöğürcü,et al.  Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures , 2014 .

[63]  Ying Liu,et al.  Analysis of the prediction capability of web search data based on the HE-TDC method ‒ prediction of the volume of daily tourism visitors , 2017 .

[64]  Irem Önder,et al.  Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities , 2017 .

[65]  Haiyan Song,et al.  Tourism Demand Forecasting , 2006 .

[66]  Maria Lexhagen,et al.  Web Usage Mining in Tourism - A Query Term Analysis and Clustering Approach , 2010, ENTER.

[67]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[68]  L. Xiaoxuan,et al.  Tourism forecasting by search engine data with noise-processing , 2016 .

[69]  KimEunice,et al.  Predicting selfie-posting behavior on social networking sites , 2016 .

[70]  Kwok-Leung Tsui,et al.  Forecasting tourist arrivals with machine learning and internet search index , 2019 .

[71]  Haiyan Song,et al.  Density tourism demand forecasting revisited , 2019, Annals of Tourism Research.

[72]  Torsten Schmidt,et al.  Forecasting private consumption: survey‐based indicators vs. Google trends , 2011 .

[73]  D. Fesenmaier,et al.  Adapting to the Internet , 2015 .

[74]  Ying Chih Chen,et al.  A Study on the Impact of SARS on the Forecast of Visitor Arrivals to China , 2007 .

[75]  Oscar Claveria,et al.  Forecasting tourism demand to Catalonia: Neural networks vs. time series models , 2014 .

[76]  M. Fuchs,et al.  Big data analytics for knowledge generation in tourism destinations – A case from Sweden , 2014 .

[77]  Gang Li,et al.  Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method , 2019 .

[78]  Bing Pan,et al.  Google Trends and tourists' arrivals: Emerging biases and proposed corrections , 2018, Tourism Management.

[79]  Paulo Rita,et al.  Forecasting tomorrow’s tourist , 2016 .

[80]  Maria Lexhagen,et al.  Business intelligence for cross-process knowledge extraction at tourism destinations , 2015, J. Inf. Technol. Tour..

[81]  E. Smeral Tourism Forecasting Performance Considering the Instability of Demand Elasticities , 2017 .

[82]  Berna Yazici,et al.  Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting , 2007 .

[83]  Cheng-Hua Wang,et al.  Support vector regression with genetic algorithms in forecasting tourism demand , 2007 .

[84]  Chun-Fu Chen,et al.  Forecasting tourism demand based on empirical mode decomposition and neural network , 2012, Knowl. Based Syst..

[85]  G. Judge,et al.  Searching for the picture: forecasting UK cinema admissions using Google Trends data , 2012 .

[86]  Mahalia Jackman,et al.  Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it! , 2015 .

[87]  Matthias Fuchs,et al.  Modelling Asian incoming tourism: A shift‐share approach , 2000 .

[88]  Haiyan Song,et al.  New developments in tourism and hotel demand modeling and forecasting , 2017 .

[89]  Sidhartha S. Padhi,et al.  Quantifying potential tourist behavior in choice of destination using Google Trends. , 2017 .

[90]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[91]  SchmidhuberJürgen Deep learning in neural networks , 2015 .

[92]  Wonho Song,et al.  Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data , 2017 .

[93]  Rob Law,et al.  A practitioners guide to time-series methods for tourism demand forecasting - a case study of Durban, South Africa , 2001 .

[94]  Michael McAleer,et al.  Forecasting tourist arrivals , 2001 .

[95]  Bing Pan,et al.  Forecasting hotel room demand using search engine data. , 2012 .

[96]  Irem Önder,et al.  Forecasting Tourism Demand with Google Trends For a Major European City Destination , 2016 .

[97]  Enric Monte,et al.  Tourism demand forecasting with neural network models: different ways of treating information. , 2015 .

[98]  Bing Pan,et al.  Predicting Hotel Demand Using Destination Marketing Organization’s Web Traffic Data , 2014 .

[99]  Rob Law,et al.  Analyzing and Forecasting Tourism Demand: A Rough Sets Approach , 2008 .

[100]  D. Blazquez,et al.  Big Data sources and methods for social and economic analyses , 2017 .

[101]  Rob Law,et al.  Tourism demand forecasting: A deep learning approach , 2019, Annals of Tourism Research.

[102]  Maria Lexhagen,et al.  Sensing the Online Social Sphere Using a Sentiment Analytical Approach , 2017 .