What is it about?
This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the incorporation of the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in neural network models. This multiple-input-multiple-output (MIMO) approach allows the generation of predictions for all visitor markets simultaneously.
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Why is it important?
The main aim of this study is to develop a new forecasting framework to improve the forecasting performance of artificial neural network (ANN) models. The results of the forecasting multiple-step ahead comparison show that: - The predictive performance of ANN models can be improved by taking into account the connections between the different markets by means of multivariate MIMO architectures. - Hybrid models, which combine supervised and non-supervised learning, are more indicated for tourism demand forecasting than models using supervised learning alone. - Radial basis function (RBF) ANN models outperform the rest of the models. Additionally, the authors developed a new forecasting accuracy measure to compare the forecasting performance between two competing models. This statistic consists on a ratio that calculates the proportion of periods in which the model under evaluation obtains a lower absolute forecasting error than the benchmark model. This measure of forecast accuracy is referred to as the percentage of periods with lower absolute error (PLAE).
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This page is a summary of: A new forecasting approach for the hospitality industry, International Journal of Contemporary Hospitality Management, October 2015, Emerald, DOI: 10.1108/ijchm-06-2014-0286.
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