A new forecasting approach for the hospitality industry

Abstract

Purpose – 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.\ud Design/methodology/approach – This multiple-input-multiple-output approach allows the generation of predictions for all visitor markets simultaneously. Official data of tourist arrivals to Catalonia (Spain) from 2001 to 2012 were used to generate forecasts for one, three and six months ahead with three different networks.\ud Findings – The study revealed that multivariate architectures that take into account the connections between different markets may improve the predictive performance of neural networks. Additionally, the authors developed a new forecasting accuracy measure and found that radial basis function networks outperform the rest of the models.\ud Research limitations/implications – This research contributes to the hospitality literature by developing an innovative framework to improve the forecasting performance of artificial intelligence\ud techniques and by providing a new forecasting accuracy measure.\ud 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\ud industry.\ud Originality/value – A multivariate neural network framework has been developed to improve forecasting accuracy, providing professionals with an innovative and practical forecasting approachPeer ReviewedPostprint (author's final draft

New developments in tourism and hotel demand modeling and forecasting

“…Chen et al, 2012;Claveria and Torra, 2014;Lin et al, 2011). As an alternative, Claveria et al (2015a), and Cuhadar et al (2014). GRNN is similar to the RBF network, being based on kernel regression.…”

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

“…A MIMO approach to regional economic modelling is particularly appropriate when the desired outputs are connected (Claveria et al 2015). In Fig.…”

“…Medeiros et al (2008) develop an ANN-GARCH model to estimate demand for international tourism also in the Balearic Islands. Claveria et al (2015) compare the forecasting performance of three ANN architectures to forecast tourist arrivals to Catalonia.…”

Forecasting tomorrow’s tourist

“…China and Spain are good examples of researched countries since they have high market shares in their continents but also portrait a high grow and a leading tourist destination, respectively. However, it should be stressed that most of the studies based on Spain used Catalonia data and were authored or co-authored by Claveria (e.g., Claveria et al, 2015), in a total of six of them published in such a narrow timeframe (from 2013 up to June 2016). In fact, this author has followed different approaches apparently based on the same data, proving the inherent potential of data-driven knowledge discovery experiments, which are hardly exhausted with the first discoveries.…”