A new forecasting approach for the hospitality industry

Purpose This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. In order to do so we develop a multivariate setting that allows incorporating the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in Neural Network models. Design/methodology/approach This multiple-input multiple-output approach allows generating predictions for all visitors markets simultaneously. Official data of tourist arrivals to Catalonia (Spain) from 2001 to 2012 are used to generate forecasts for 1, 3 and 6 months ahead with three different networks. Findings – The study reveals that multivariate architectures that take into account the connections between different markets may improve the predictive performance of neural networks. Additionally, we develop a new forecasting accuracy measure and find that radial basis function networks outperform the rest of the models. Research limitations/implications This research contributes to the hospitality literature by developing an innovative framework to improve the forecasting performance of artificial intelligence techniques and by providing a new forecasting accuracy measure. Practical implications The proposed forecasting approach may prove very useful for planning purposes, helping managers to anticipate the evolution of variables related to the daily activity of the industry. Originality/value – A multivariate neural network framework is developed to improve forecasting accuracy, providing professionals with an innovative and practical forecasting approach.