Cang, S., 2011. A Nonlinear Tourism Demand Forecast Combination Model. Tourism Economics, 17 (1), pp. 5-20.
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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.
|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:||Social Sciences > Economics|
Social Sciences > Tourism
|Group:||School of Tourism > International Centre for Tourism and Hospitality Research|
|Deposited By:||Dr Shuang Cang|
|Deposited On:||31 Jan 2011 10:15|
|Last Modified:||07 Mar 2013 15:41|
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