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

Cang, Shuang (2014) A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non-linear Combination. International Journal of Tourism Research, 16 (6). pp. 596-607. ISSN 1099-2340

Full text not available from this repository.

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.

Item Type:	Article
Uncontrolled Keywords:	neural networks, time series, combination forecasts, tourism demand
Subjects:	N800 Tourism, Transport and Travel
Department:	Faculties > Business and Law > Newcastle Business School
Depositing User:	Becky Skoyles
Date Deposited:	07 Dec 2018 09:04
Last Modified:	19 Nov 2019 09:50
URI:	http://nrl.northumbria.ac.uk/id/eprint/37118

Downloads