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

Shuang Cang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)596-607
Number of pages12
JournalInternational Journal of Tourism Research
Volume16
Issue number6
Early online date30 May 2013
DOIs
Publication statusPublished - Nov 2014

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