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A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non-linear Combination



This paper investigates the combination of individual forecasting models and their roles in improving forecasting accuracy and proposes two non-linear combination forecasting models using Radial Basis Function and Support Vector Regression neural networks. These two non-linear combination models plus the standard Multi-layer Perceptron neural network-based non-linear combination model are examined and compared with the linear combination models. The UK inbound tourism quarterly arrival data is used and the empirical results demonstrate that the proposed non-linear combination models are robust and outperform the linear combination models that currently dominate in the tourism forecasting literature. Copyright © 2013 John Wiley & Sons, Ltd.