A non-linear tourism demand forecast combination model

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Authors: Cang, S.

Journal: Tourism Economics

Volume: 17

Pages: 5-20

ISSN: 1354-8166

DOI: 10.5367/te.2011.0031

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.

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Authors: Cang, S.

Journal: Tourism Economics

Volume: 17

Issue: 1

Pages: 5-20

ISSN: 1354-8166

DOI: 10.5367/te.2011.0031

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 nonlinear MLPNN combination model is robust, powerful and can provide better performance at predicting arrivals than linear combination models.

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