A Non-Linear Tourism Demand Forecast Combination Model

Cang, Shuang (2011) A Non-Linear Tourism Demand Forecast Combination Model. Tourism Economics, 17 (1). pp. 5-20. ISSN 1354-8166

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Abstract

It has been demonstrated in the tourism literature that a combination of individual tourism forecasting models can provide better performance than individual forecasting models. However, the linear combination uses only inputs that have a linear correlation to the actual outputs. This paper proposes a non-linear combination method using multilayer perceptron neural networks (MLPNN), which can map the non-linear relationship between inputs and outputs. UK inbound tourism quarterly arrivals data by purpose of visit are used for this case study. The empirical results show that the proposed non-linear MLPNN combination model is robust, powerful and can provide better performance at predicting arrivals than linear combination models.

Item Type:	Article
Uncontrolled Keywords:	tourism demand forecasting, multilayer perceptron neural networks, support vector regression neural networks, autoregressive integrated moving average, Winters' multiplicative exponential smoothing, combination forecasts
Subjects:	N800 Tourism, Transport and Travel
Department:	Faculties > Business and Law > Newcastle Business School
Depositing User:	Becky Skoyles
Date Deposited:	18 Dec 2018 12:20
Last Modified:	19 Nov 2019 09:52
URI:	http://nrl.northumbria.ac.uk/id/eprint/37327

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