Abstract
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.
Highlights
During the last decades, tourism has been developed into one of the fastest growing industries worldwide; it constitutes a significant factor of the economic growth of a country
It is based on a different philosophy from other regression models, as it tries to fit the best line within a predefined error value, while other regression models try to minimize the error between the predicted and the actual value
A methodology for developing a tourism time-series model is proposed based on weight-constrained neural networks
Summary
Tourism has been developed into one of the fastest growing industries worldwide; it constitutes a significant factor of the economic growth of a country. Artificial Neural Networks (ANNs) are widely accepted as probably the most dominant machine learning models which have been found to be more accurate than other prediction models [12,13] Due to their self-learning capability and their universal approximation ability, they can efficiently capture the nonlinearity of samples in the data; they have been successfully applied in order to gain a more meticulous view of tourism forecasting. We conducted a series of experiments and compared the forecasting performance of these new forecasting models against classical ANNs as well as other state-of-the-art regression models For these experiments, we utilized official statistical data from the Hellenic Statistical Authority regarding domestic and foreign tourists arrivals in Greek hotels from 2004 to 2017.
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