A novel approach to model selection in tourism demand modeling

Abstract

In many studies on tourism demand modeling, the main conclusion is that none of the considered modeling approaches consistently outperforms the others. We consider Seasonal AutoRegressive Integrated Moving Average, ν-Support Vector Regression, and multi-layer perceptron type Neural Network models and optimize their parameters using different techniques for each and compare their performances on monthly tourist arrival data to Turkey from different countries. Based on these results, this study proposes a novel approach to model selection for a given tourism time series. Our approach is based on identifying the components of the given time series using structural time series modeling. Using the identified components we construct a decision tree and obtain a rule set for model selection.