Abstract
Accurate tourism demand forecasting systems are very important in tourism planning, especially in high tourist countries and regions within. In this paper we investigate the problem of accurate tourism demand prediction using nonlinear regression techniques based on Artificial Neural Networks (ANN). The relative accuracy of the Multilayer Perceptron (MLP) and Support Vector regression (SVR) in tourist occupancy data is investigated and compared to simple Linear Regression (LR) models. The relative performance of the MLP and SVR models is also compared to each other. For this, the data collected for a period of 8 years (2005–2012) showing tourism occupancy of the hotels of the Western Region of Greece is used. Extensive experiments have shown that the SVM regressor with the RBF kernel (SVR-RBF) outperforms the other forecasting models when tested for a wide range of forecast horizon (1–24 months) presenting very small and stable prediction error compared to SVR-POLY, MLP, as well as the simple LR models.
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Acknowledgements
This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund.
The data that involves the monthly occupancy of all tourist accommodations of both foreign and domestic tourists came from the official records of the Hellenic Statistical Authority (EL. STAT., www.statistics.gr).
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Koutras, A., Panagopoulos, A., Nikas, I.A. (2016). Evaluating the Performance of Linear and Nonlinear Models in Forecasting Tourist Occupancy in the Region of Western Greece. In: Katsoni, V., Stratigea, A. (eds) Tourism and Culture in the Age of Innovation. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-27528-4_26
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DOI: https://doi.org/10.1007/978-3-319-27528-4_26
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