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A non-linear tourism demand forecast combination model.
- Source :
- Tourism Economics; Feb2011, Vol. 17 Issue 1, p5-20, 16p
- Publication Year :
- 2011
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- TOURISM
MATHEMATICAL models
ECONOMIC forecasting
ARTIFICIAL neural networks
Subjects
Details
- Language :
- English
- ISSN :
- 13548166
- Volume :
- 17
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Tourism Economics
- Publication Type :
- Academic Journal
- Accession number :
- 57566098
- Full Text :
- https://doi.org/10.5367/te.2011.0031