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.