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

This source preferred by Shuang Cang

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Authors: Cang, S.

Journal: International Journal of Tourism Research

eISSN: 1522-1970

ISSN: 1099-2340

DOI: 10.1002/jtr.1953

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. © 2013 John Wiley & Sons, Ltd.

This data was imported from Web of Science (Lite):

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

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