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 nonlinear MLPNN combination model is robust, powerful and can provide better performance at predicting arrivals than linear combination models.
Original language | English |
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Pages (from-to) | 5-20 |
Number of pages | 16 |
Journal | Tourism Economics |
Volume | 17 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Feb 2011 |
Keywords
- Autoregressive integrated moving average
- Combination forecasts
- Multilayer perceptron neural networks
- Support vector regression neural networks
- Tourism demand forecasting
- Winters' multiplicative exponential smoothing