A Non-Linear Tourism Demand Forecast Combination Model

Shuang Cang

Tourism Economics, 2011, vol. 17, issue 1, 5-20

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.

Keywords: tourism demand forecasting; multilayer perceptron neural networks; support vector regression neural networks; autoregressive integrated moving average; Winters' multiplicative exponential smoothing; combination forecasts (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:17:y:2011:i:1:p:5-20

DOI: 10.5367/te.2011.0031

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