Ex Ante Tourism Forecasting Assessment

Although numerous studies have focused on forecasting international tourism demand, minimal light has been shed on the factors influencing the accuracy of real-world ex ante forecasting. This study evaluates the forecasting errors across various prediction horizons by analyzing the annually published forecasts of the Pacific Asia Tourism Association (PATA) from 2013 to 2017, comprising 765 origin–destination pairs covering 31 destinations in the region. The regression analysis shows that the variation in tourism demand and gross domestic product (GDP), covariation between tourism demand and GDP, order of lagged variables, origin, destination, and forecasting method all have significant effects on the forecasting accuracy over different horizons. This suggests that tourism forecasting should account for these factors in the future.

[1]  Chang Liu,et al.  Forecasting Tourism Demand with an Improved Mixed Data Sampling Model , 2020 .

[2]  Haiyan Song,et al.  A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting , 2019, Annals of Tourism Research.

[3]  Gang Li,et al.  The combination of interval forecasts in tourism , 2019, Annals of Tourism Research.

[4]  Yang Yang,et al.  Spatial-temporal forecasting of tourism demand , 2019, Annals of Tourism Research.

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

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

[7]  Jason Li Chen,et al.  Tourism forecasting: A review of methodological developments over the last decade , 2018, Tourism Economics.

[8]  Dimitrios Buhalis,et al.  Forecasting tourist arrivals at attractions: Search engine empowered methodologies , 2018, Tourism Economics.

[9]  Haiyan Song,et al.  Pooling in Tourism Demand Forecasting , 2018, Journal of Travel Research.

[10]  Lakshmi Sravanti,et al.  Goodness of fit , 2017, Indian journal of psychiatry.

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

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

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

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

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

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

[17]  Melda Akın,et al.  A novel approach to model selection in tourism demand modeling , 2015 .

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

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

[20]  Vera Shanshan Lin,et al.  Accuracy and bias of experts’ adjusted forecasts , 2014 .

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

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

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

[24]  Zvi Schwartz,et al.  The Accuracy of Tourism Forecasting and Data Characteristics: A Meta-Analytical Approach , 2013 .

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

[26]  Haiyan Song,et al.  Assessing the Impacts of the Global Economic Crisis and Swine Flu on Inbound Tourism Demand in the United Kingdom , 2012 .

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

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

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

[30]  N. Vanhove,et al.  Forecasting Tourism Demand , 2011 .

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

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

[33]  J. Scott Armstrong,et al.  Selecting Forecasting Methods , 2009 .

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

[35]  Chao-Hung Wang,et al.  Constructing and applying an improved fuzzy time series model: Taking the tourism industry for example , 2008, Expert Syst. Appl..

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

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

[38]  Michael A. Salsburg,et al.  Modeling and Forecasting , 2007, Int. CMG Conference.

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

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

[41]  Haiyan Song,et al.  Bayesian models for tourism demand forecasting. , 2006 .

[42]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[43]  J. Tokle,et al.  An Overview of Forecasting Error among International Manufacturers , 2006 .

[44]  Haiyan Song,et al.  Forecasting international tourist flows to Macau , 2006 .

[45]  Gang Li,et al.  Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting , 2006 .

[46]  Maria M. De Mello,et al.  Testing Alternative Dynamic Systems for Modelling Tourism Demand , 2005 .

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

[48]  Gang Li,et al.  Modeling Tourism Demand: A Dynamic Linear AIDS Approach , 2004 .

[49]  Haiyan Song,et al.  Tourism Demand Modeling: A Time-Varying Parameter Approach , 2003 .

[50]  J. Armstrong,et al.  Evaluating Forecasting Methods , 2001 .

[51]  R. Law.  Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting , 2000 .

[52]  R. Koenker,et al.  Goodness of Fit and Related Inference Processes for Quantile Regression , 1999 .

[53]  Rob Law,et al.  A neural network model to forecast Japanese demand for travel to Hong Kong , 1999 .

[54]  Ruth A. Schmidt,et al.  short-term forecasting , 2022, The Fairchild Books Dictionary of Fashion.

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

[56]  P. Goodwin,et al.  Improving judgmental time series forecasting: A review of the guidance provided by research , 1993 .

[57]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[58]  R. Armstrong.  Review of Research. , 1990 .

[59]  Adamantios Diamantopoulos,et al.  Managerial intervention in forecasting. An empirical investigation of forecast manipulation , 1986 .

[60]  Steven P. Schnaars,et al.  Situational Factors Affecting Forecast Accuracy , 1984 .