Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

Regional Forecasting with Support Vector Regressions: The Case of Spain

This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian kernel shows the best forecasting performance. The best predictions are obtained for longer forecast horizons, which suggest the suitability of machine learning techniques for medium and long term forecasting

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

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.Peer ReviewedPostprint (author's final draft

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

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level

"Applications of Intelligent Systems in Tourism: Relevant Methods"

"This article presents a literature review of Intelligent Systems applications in Tourism in different parts of the world. The review focuses on the most relevant methods in free and paid databases, in English and Spanish. Most of the works deal with methodologies based on artificial intelligence, such as expert systems, fuzzy logic, machine learning, data mining, neural networks, genetic algorithms. In the review, several characteristics of the systems have been taken into account, such as, knowledge base, actors in the operation of the system, types of tourists, usefulness or not in space and time. According to the review it was found that most of the researches are deterministic models, the most used methodology in tourism are the expert systems based on rules, observing an emerging innovation in metaheuristics, mainly genetic algorithms and intelligent systems with knowledge base based on optimization methods for route choice or optimal visit plan. It is expected that this work serves to give a general perspective on the application of intelligent systems in the area of tourism, as well as a work that consolidates background for work in this area of research.

Time series features and machine learning forecasts

In this study we combine the results of two independent analyses to position Spanish regions according to both the characteristics of the time series of international tourist arrivals and the accuracy of predictions of arrivals at the regional level. We apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the series and calculate the main time series features. Predictions are generated with several machine learning models in a recursive multi-step-ahead forecasting experiment. Finally, we summarize all the information from the two previous experiments using categorical principal component analysis. By overlapping the distribution of the regions and the component loadings of each variable along both dimensions, we observe that entropy and dispersion show an inverse relation with forecast accuracy, but the interactions between the rest of the features and accuracy are heavily dependent on the forecast horizon. On this evidence, we conclude that in order to increase forecast accuracy of tourist arrivals at the regional level, model selection should be region-specific and based on the forecast horizon

New Developments in Tourism and Hotel Demand Modeling and Forecasting

Abstract Purpose The purpose of the study is to review recent studies published from 2007-2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field. Design/Methodology/approach Articles on tourism and hotel demand modeling and forecasting published in both science citation index (SCI) and social science citation index (SSCI) journals were identified and analyzed. Findings This review found that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area. Research limitations/implications The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implications This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/value The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions