A non-linear tourism demand forecast combination model

作者：Cang Shuang

来源：Tourism Economics, 2011, 17(1): 5-20.

DOI：10.5367/te.2011.0031

• tourism demand forecasting
• multilayer perceptron neural networks
• support vector regression neural networks
• autoregressive integrated moving average
• Winters' multiplicative exponential smoothing
• combination forecasts

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.

• 出版日期2011-2