Tourism forecasting: A review of methodological developments over the last decade

This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

[1]  Vera Shanshan Lin,et al.  Modeling and Forecasting Inbound Tourism Demand for Long-Haul Markets of Beijing , 2013 .

[2]  John T. Coshall,et al.  A management orientated approach to combination forecasting of tourism demand , 2011 .

[3]  E. Ghysels,et al.  Série Scientifique Scientific Series Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies , 2022 .

[4]  James B. McDonald,et al.  Time Series Prediction With Genetic‐Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical Models , 1999, Comput. Intell..

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

[6]  John T. Coshall,et al.  Combining volatility and smoothing forecasts of UK demand for international tourism , 2009 .

[7]  Gang Li,et al.  Forecasting tourist arrivals using time-varying parameter structural time series models , 2011 .

[8]  Miao-Sheng Chen,et al.  Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system , 2009, Expert Systems with Applications.

[9]  Milos Bigovic,et al.  Demand Forecasting within Montenegrin Tourism Using Box-Jenkins Methodology for Seasonal ARIMA Models , 2012 .

[10]  Irem Önder,et al.  Forecasting city arrivals with Google Analytics , 2016 .

[11]  M. Dewally,et al.  Internet Investment Advice: Investing with a Rock of Salt , 2000 .

[12]  Bernard J. Morzuch,et al.  Evaluating Time-Series Models to Forecast the Demand for Tourism in Singapore , 2005 .

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

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

[15]  Cao Guo-hua,et al.  Seasonal SVR with FOA algorithm for single-step and multi-step ahead forecasting in monthly inbound tourist flow , 2016 .

[16]  A. Saayman,et al.  Non-linear models for tourism demand forecasting , 2017 .

[17]  Bing Pan,et al.  Forecasting Destination Weekly Hotel Occupancy with Big Data , 2017 .

[18]  Stephen F. Witt,et al.  An Empirical Study of Forecast Combination in Tourism , 2009 .

[19]  George Athanasopoulos,et al.  Modelling and Forecasting Australian Domestic Tourism , 2006 .

[20]  Rob Law,et al.  A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong , 2012, Expert Syst. Appl..

[21]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[22]  Rafael Boix,et al.  Sources of growth and competitiveness of local tourist production systems: an application to Italy (1991–2001) , 2008 .

[23]  George Athanasopoulos,et al.  Hierarchical forecasts for Australian domestic tourism , 2009 .

[24]  F. Capone,et al.  Spatial Spillovers and Employment Dynamics in Local Tourist Systems in Italy (1991–2001) , 2009 .

[25]  Esmaeil Hadavandi,et al.  Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series , 2013, Knowl. Based Syst..

[26]  Julian K. Ayeh,et al.  ‘Estimating tomorrow’s tourist arrivals’: forecasting the demand for China’s tourism using the general-to-specific approach , 2011 .

[27]  João Paulo Teixeira,et al.  Tourism demand modelling and forecasting with artificial neural network models: The Mozambique case study , 2016 .

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

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

[30]  Emmanuel Sirimal Silva,et al.  Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques , 2015 .

[31]  Massimiliano Marcellino,et al.  Midas Vs. Mixed-Frequency VAR: Nowcasting GDP in the Euro Area , 2009 .

[32]  Haiyan Song,et al.  An Assessment of Combining Tourism Demand Forecasts over Different Time Horizons , 2008 .

[33]  T. Yalcinoz,et al.  Implementing soft computing techniques to solve economic dispatch problem in power systems , 2008, Expert Syst. Appl..

[34]  Lin Tang,et al.  Forecasting tourism demand by extracting fuzzy Takagi-Sugeno rules from trained SVMs , 2016, CAAI Trans. Intell. Technol..

[35]  Haiyan Song,et al.  Recent Developments in Econometric Modeling and Forecasting , 2005 .

[36]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[37]  Manuel Vanegas Co-Integration and Error Correction Estimation to Forecast Tourism in El Salvador , 2013 .

[38]  Peter Fuleky,et al.  Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism , 2017 .

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

[40]  Haiyan Song,et al.  Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system , 2013 .

[41]  Fong-Lin Chu,et al.  A fractionally integrated autoregressive moving average approach to forecasting tourism demand , 2007, Tourism Management.

[42]  Guohua Cao,et al.  Seasonal SVR with FOA algorithm for single-step and multi-step ahead forecasting in monthly inbound tourist flow , 2016, Knowl. Based Syst..

[43]  Xiankai Huang,et al.  The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City , 2017 .

[44]  Gang Li,et al.  An Assessment of Combining Tourism Demand Forecasts over Different Time Horizons , 2008 .

[45]  Brian Archer,et al.  Demand forecasting and estimation. , 1987 .

[46]  Fong-Lin Chu,et al.  Using a logistic growth regression model to forecast the demand for tourism in Las Vegas , 2014 .

[47]  Xin Li,et al.  Forecasting tourism demand with composite search index : , 2016 .

[48]  Shen Liu,et al.  Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals , 2011 .

[49]  Emmanuel Sirimal Silva,et al.  Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis , 2015 .

[50]  Fong-Lin Chu,et al.  Forecasting tourism: a combined approach , 1998 .

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

[52]  Kevin K. F. Wong,et al.  Bias‐corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting , 2010 .

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

[54]  Brad M. Barber,et al.  Journal of Economic Perspectives—Volume 15, Number 1—Winter 2001—Pages 41–54 The Internet and the Investor , 2022 .

[55]  Xin Yang,et al.  Forecasting Chinese tourist volume with search engine data , 2015 .

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

[57]  Irem Önder,et al.  Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data , 2015 .

[58]  Kevin K. F. Wong,et al.  Tourism forecasting: To combine or not to combine? , 2007 .

[59]  George Athanasopoulos,et al.  Multivariate Exponential Smoothing for Forecasting Tourist Arrivals , 2012 .

[60]  Vera Shanshan Lin,et al.  Modeling and Forecasting Chinese Outbound Tourism: An Econometric Approach , 2015 .

[61]  R. Law,et al.  The Methodological Progress of Tourism Demand Forecasting: A Review of Related Literature , 2011 .

[62]  Fong-Lin Chu,et al.  Analyzing and forecasting tourism demand with ARAR algorithm , 2008 .

[63]  Enric Monte,et al.  A new forecasting approach for the hospitality industry , 2015 .

[64]  Gang Li,et al.  Combination forecasts of international tourism demand , 2011 .

[65]  Fong-Lin Chu,et al.  Forecasting tourism demand with ARMA-based methods. , 2009 .

[66]  Kuan-Yu Chen,et al.  Combining linear and nonlinear model in forecasting tourism demand , 2011, Expert Syst. Appl..

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

[68]  Wei-Chiang Hong,et al.  Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm , 2015, Appl. Soft Comput..

[69]  Konstantinos Nikolopoulos,et al.  The Tourism Forecasting Competition , 2011 .

[70]  Aoife Hanley,et al.  Spillover effects in long‐haul visitors between two regions , 2005 .

[71]  Modelling and forecasting daily international mass tourism to Peru , 2010 .

[72]  Amir F. Atiya,et al.  Combination of long term and short term forecasts, with application to tourism demand forecasting , 2011 .

[73]  Shuang Cang,et al.  A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non‐linear Combination , 2014 .

[74]  Andrea Mervar,et al.  Forecasting disaggregated tourist arrivals in Croatia , 2017 .

[75]  K. Nowman,et al.  Forecasting Overseas Visitors to the UK Using Continuous Time and Autoregressive Fractional Integrated Moving Average Models with Discrete Data , 2012 .

[76]  Chi Kin Chan,et al.  Tourism forecast combination using the CUSUM technique. , 2010 .

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

[78]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[79]  Konstantinos Drakos,et al.  Regional Effects of Terrorism on Tourism in Three Mediterranean Countries , 2003 .

[80]  Wangshu Sun,et al.  Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China , 2016 .

[81]  J. Xander,et al.  Combining time-series and econometric forecast of tourism activity , 1984 .

[82]  Fong-Lin Chu,et al.  A piecewise linear approach to modeling and forecasting demand for Macau tourism. , 2011 .

[83]  Yi-Hui Liang,et al.  Forecasting models for Taiwanese tourism demand after allowance for Mainland China tourists visiting Taiwan , 2014, Comput. Ind. Eng..

[84]  Shuang Cang A Non-Linear Tourism Demand Forecast Combination Model , 2011 .

[85]  Andrea Guizzardi,et al.  Real-time forecasting regional tourism with business sentiment surveys , 2015 .

[86]  Marcos álvarez Díaz,et al.  Forecasting Daily Air Arrivals in Mallorca Island Using Nearest Neighbour Methods , 2007 .

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

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

[89]  Daniel Santamaria,et al.  Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists' origin. , 2012 .

[90]  Koon Nam Henry Lee Forecasting long-haul tourism demand for Hong Kong using error correction models , 2011 .

[91]  Christina Beneki,et al.  Signal Extraction and Forecasting of the UK Tourism Income Time Series. A Singular Spectrum Analysis Approach , 2012 .

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

[93]  Chukiat Chaiboonsri,et al.  International Tourists Arrival to Thailand: Forecasting by Non-linear Model☆ , 2014 .

[94]  Kevin K. F. Wong,et al.  A Spatial Econometric Approach to Model Spillover Effects in Tourism Flows , 2012 .