Forecasting Tourism Demand Using Linear and Nonlinear Prediction Models

Athanasios Koutras, Alkiviadis Panagopoulos and Ioannis A. Nikas
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Athanasios Koutras: Technological Educational Institute ofWestern Greece, Department of Informatics and Mass Media, Greece
Alkiviadis Panagopoulos: Technological Educational Institute ofWestern Greece, Department of Tourism Management, Greece
Ioannis A. Nikas: Technological Educational Institute ofWestern Greece, Department of Tourism Management, Greece

Academica Turistica - Tourism and Innovation Journal, 2016, vol. 9, issue 1, 85-98

Abstract: In this paper, we propose and evaluate linear and nonlinear prediction models based on Artificial Neural Networks (ANN) for tourism demand in the accommodation industry. For efficient forecasting, the Multilayer Perceptron (MLP), Support Vector Regression (SVR) and Linear Regression (LR) methods that utilize two different feature sets for training have been used. Themajor contribution of the proposed models is focused mainly on better forecasting accuracy and lower cost effort. The relative accuracy of the Multilayer Perceptron (MLP) and Support Vector Regression (SVR) in tourism occupancy data is investigated and compared to simple Linear Regression (LR) models. The relative performance of the MLP and SVR models are also compared to each other. Data collected over a period of eight years (2005–2012) showing tourism occupancy and the number of overnight stays in the hotels of the Western Region of Greece is used. Extensive experiments have shown that for time series describing a subset of the number of overnight stays, the SVR regressor with the RBF kernel (SVR-RBF), as well as simple LR models, and the MLP regressor for occupancy time series respectively, outperform other forecasting models, when tested for a wide range of forecast horizons (1–24months) and present very small and stable prediction errors.

Keywords: support vector regression; multilayer perceptron; artificial neural networks; tourism demand forecasting; forecasting model; time-series (search for similar items in EconPapers)
Date: 2016
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