Models of Artificial Neural Networks Applied to Demand Forecasting in Nonconsolidated Tourist Destinations

This article focuses on a new proposed artificial neural network (ANN) model for tourism demand forecasting using time-series which, unlike previous models, uses different seasons of arrivals and values of months with similar behavior as input variables and achieves a forecast up to a year in advance. We demonstrate the validity and greater precision of the proposed model in forecasting a nonconsolidated destination with marked seasonality, which has been scarcely dealt with in other research. We achieve a comparatively greater quality of results and a longer period in advance than previously used auto-regressive integrated moving average (ARIMA) and ANN models. Highly accurate results were also obtained in destinations such as Portugal, which also proves its validity for mature destinations.

[1]  R. Fisher.  Statistical methods for research workers , 1927, Protoplasma.

[2]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[3]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[4]  D. Rubin.  Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician , 1984 .

[5]  Richard M. Helmer,et al.  An Exposition of the Box-Jenkins Transfer Function Analysis with an Application to the Advertising-Sales Relationship , 1977 .

[6]  Sally Galbraith,et al.  Guidelines for the design of clinical trials with longitudinal outcomes. , 2002, Controlled clinical trials.

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

[8]  D. Rindskopf,et al.  The value of latent class analysis in medical diagnosis. , 1986, Statistics in medicine.

[9]  A. Satorra,et al.  Corrections to test statistics and standard errors in covariance structure analysis. , 1994 .

[10]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[11]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[12]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[13]  Iven Van Mechelen,et al.  Constrained Latent Class Analysis of Three-Way Three-Mode Data , 2002, J. Classif..

[14]  João Paulo Teixeira,et al.  New approach of the ann methodology for forecasting time series: use of time index , 2009 .

[15]  A. Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[16]  Rudy Ligtvoet,et al.  Latent class models for testing monotonicity and invariant item ordering for polytomous items. , 2012, The British journal of mathematical and statistical psychology.

[17]  Wei-Chiang Hong,et al.  An Improved Neural Network Model in Forecasting Arrivals , 2005 .

[18]  Xiao-Li Meng,et al.  Posterior Predictive $p$-Values , 1994 .

[19]  J. Hagenaars.  Latent Structure Models with Direct Effects between Indicators , 1988 .

[20]  M. J. Bayarri,et al.  P Values for Composite Null Models , 2000 .

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

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

[23]  Coskun Hamzaçebi,et al.  Improving artificial neural networks' performance in seasonal time series forecasting , 2008, Inf. Sci..

[24]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[25]  Iven Van Mechelen,et al.  A Bayesian approach to the selection and testing of mixture models , 2003 .

[26]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[27]  Shelby J. Haberman,et al.  A Warning on the Use of Chi-Squared Statistics with Frequency Tables with Small Expected Cell Counts , 1988 .

[28]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[29]  N. Kulendran,et al.  Forecasting China's Monthly Inbound Travel Demand , 2002 .

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

[31]  Çagdas Hakan Aladag,et al.  A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization , 2012, Neural Processing Letters.

[32]  Chaohui Wang,et al.  Predicting tourism demand using fuzzy time series and hybrid grey theory. , 2004 .

[33]  S. Haberman.  Analysis of qualitative data , 1978 .

[34]  H. Hoijtink.  Constrained Latent Class Analysis Using the Gibbs Sampler and Posterior Predictive P-values: Applications to Educational Testing , 1998 .

[35]  Geert H. van Kollenburg,et al.  A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models , 2013, Adv. Data Anal. Classif..

[36]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

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

[38]  Shintaro Okazaki,et al.  A Latent Class Analysis of Spanish Travelers’ Mobile Internet Usage in Travel Planning and Execution , 2015 .

[39]  L. A. Goodman.  Exploratory latent structure analysis using both identifiable and unidentifiable models , 1974 .

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

[41]  Rob Law,et al.  Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. , 2002 .

[42]  Brenda R. J. Jansen,et al.  Statistical Test of the Rule Assessment Methodology by Latent Class Analysis , 1997 .

[43]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

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

[45]  J. Vermunt.  Latent Class Models , 2004 .

[46]  S. Raudenbush,et al.  Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. , 2001, Psychological methods.

[47]  James M. Robins,et al.  Asymptotic Distribution of P Values in Composite Null Models , 2000 .

[48]  N. Hjort,et al.  Post-Processing Posterior Predictive p Values , 2006 .

[49]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[50]  Ralf Bender,et al.  Generating survival times to simulate Cox proportional hazards models , 2005, Statistics in medicine.

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

[52]  I. Bongers,et al.  Treatment engagement in adolescents with severe psychiatric problems: a latent class analysis , 2013, European Child & Adolescent Psychiatry.

[53]  J. Moreno,et al.  Artificial neural networks applied to forecasting time series , 2011 .

[54]  N D Holmquist,et al.  Variability in classification of carcinoma in situ of the uterine cervix. , 1967, Archives of pathology.

[55]  Rob Law,et al.  The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques , 2001 .

[56]  S. F. Witt,et al.  Univariate versus multivariate time series forecasting: an application to international tourism demand , 2003 .

[57]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[58]  Herbert Hoijtink,et al.  Applications of confirmatory latent class analysis in developmental psychology , 2005 .

[59]  M. Reiser,et al.  3. A Goodness-of-Fit Test for the Latent Class Model When Expected Frequencies are Small , 1999 .

[60]  João Paulo Teixeira,et al.  Modelling tourism demand: a comparative study between artificial neural networks and the Box-Jenkins methodology , 2008 .

[61]  Çagdas Hakan Aladag,et al.  A new linear & nonlinear artificial neural network model for time series forecasting , 2013, Decis. Support Syst..

[62]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[63]  D. Cox.  Regression Models and Life-Tables , 1972 .

[64]  Jay Magidson,et al.  Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax 1 , 2016 .

[65]  Jeroen K. Vermunt,et al.  Factor Analysis with Categorical Indicators: A Comparison Between Traditional and Latent Class Approaches , 2005 .

[66]  Anton K. Formann,et al.  Latent class model diagnostics - a review and some proposals , 2003, Comput. Stat. Data Anal..

[67]  J. Pannekoek,et al.  Bootstrapping Goodness-of-Fit Measures in Categorical Data Analysis , 1996 .

[68]  Geert H. van Kollenburg,et al.  Assessing Model Fit in Latent Class Analysis When Asymptotics Do Not Hold , 2015 .

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

[70]  Andrew Gelman,et al.  Two simple examples for understanding posterior p-values whose distributions are far from uniform , 2013 .

[71]  S. Crow,et al.  Latent class analysis of eating disorders: relationship to mortality. , 2012, Journal of abnormal psychology.

[72]  J. R. Landis,et al.  An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. , 1977, Biometrics.

[73]  M. Dufour,et al.  Are Poker Players All the Same? Latent Class Analysis , 2015, Journal of Gambling Studies.

[74]  Bruce W. Turnbull,et al.  A latent class mixed model for analysing biomarker trajectories with irregularly scheduled observations. , 2000, Statistics in medicine.

[75]  B. Muthén,et al.  How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power , 2002 .

[76]  B. H. Thomas,et al.  Effectiveness of home visitation by public-health nurses in prevention of the recurrence of child physical abuse and neglect: a randomised controlled trial , 2005, The Lancet.

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

[78]  William N. Venables,et al.  An Introduction To R , 2004 .

[79]  P L Fidler,et al.  Goodness-of-Fit Testing for Latent Class Models. , 1993, Multivariate behavioral research.

[80]  Mirjam Moerbeek,et al.  Powerful and Cost-Efficient Designs for Longitudinal Intervention Studies With Two Treatment Groups , 2008 .

[81]  John B. Willett,et al.  It’s About Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events , 1993 .

[82]  Albert Maydeu-Olivares,et al.  Limited Information Goodness-of-fit Testing in Multidimensional Contingency Tables , 2005 .

[83]  Xiao-Li Meng,et al.  POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES , 1996 .

[84]  Mirjam Moerbeek,et al.  Power Analysis for Trials With Discrete-Time Survival Endpoints , 2012 .

[85]  E. Samuel-Cahn,et al.  P Values as Random Variables—Expected P Values , 1999 .

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

[87]  Eric R. Ziegel,et al.  Survival analysis using the SAS system , 1995 .

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

[89]  J. Singer,et al.  Applied Longitudinal Data Analysis , 2003 .