A comparative analysis of three types of tourism demand forecasting models: Individual, linear combination and non-linear combination

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Authors: Cang, S.

Journal: International Journal of Tourism Research

Volume: 16

Issue: 6

Pages: 596-607

eISSN: 1522-1970

ISSN: 1099-2340

DOI: 10.1002/jtr.1953

© 2013 John Wiley & Sons, Ltd. 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.

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