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Open AccessArticle

Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study

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Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
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Department of Electrical & Computer Engineering, University of Peloponnese, GR 241-00 Patras, Greece
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Department of Business Administration (LAIQDA Lab), University of Peloponnese, GR 241-00 Kalamata, Greece
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Department of Accounting & Finance, University of Peloponesse, GR 241-00 Kalamata, Greece
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(9), 1005; https://doi.org/10.3390/electronics8091005
Received: 10 August 2019 / Revised: 30 August 2019 / Accepted: 6 September 2019 / Published: 8 September 2019
(This article belongs to the Section Computer Science & Engineering)
Tourism forecasting is a significant tool/attribute in tourist industry in order to provide for careful planning and management of tourism resources. Although accurate tourist volume prediction is a very challenging task, reliable and precise predictions offer the opportunity of gaining major profits. Thus, the development and implementation of more sophisticated and advanced machine learning algorithms can be beneficial for the tourism forecasting industry. In this work, we explore the prediction performance of Weight Constrained Neural Networks (WCNNs) for forecasting tourist arrivals in Greece. WCNNs constitute a new machine learning prediction model that is characterized by the application of box-constraints on the weights of the network. Our experimental results indicate that WCNNs outperform classical neural networks and the state-of-the-art regression models: support vector regression, k-nearest neighbor regression, radial basis function neural network, M5 decision tree and Gaussian processes. View Full-Text
Keywords: weight-constrained neural networks; constrained optimization; time-series; machine learning; tourism forecasting weight-constrained neural networks; constrained optimization; time-series; machine learning; tourism forecasting
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Livieris, I.E.; Pintelas, E.; Kotsilieris, T.; Stavroyiannis, S.; Pintelas, P. Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study. Electronics 2019, 8, 1005.

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