Comparison of linear regression and neural network models forecasting tourist arrivals to Turkey

Comparison of linear regression and neural network models forecasting tourist arrivals to Turkey

Selcuk , Cankurt
Subasi, Abdulhamit

This paper develops statistical and machine learning methods for estimating tourist arrivals which is one of the donnée for planning the sustainable tourism development. Tourism is arguably one of the world's largest and fastest growing industries. Sustainable tourism development is one of the most promising generators of the sustainable economic development. Realistic tourism projections based on accurate tourism forecasting contribute much for the sustainable tourism development. The challenge of the planning and developing sustainable tourism is to see as the complex paradigm but one of the starting points is the accurate forecasting tourist arrivals. In this study, linear regression and neural network multilayer perceptron (MLP) implementations are considered to make multivariate tourism forecasting for Turkey. Comparison of forecasting performances in terms of correlation coefficient (R), relative absolute error (RAE) and root relative squared error (RRSE) measurements shows that MLP model for regression gives a better performance. Keywords: Tourism forecasting; Tourism demand modelling; Time series; Linear regression; Neural networks; Multilayer perceptron; Multivariate tourism forecasting.

Conference or Workshop Item
PeerReviewed

2012-05-31

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