The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourism demand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time series methods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals from all the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural networks models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour, we also find that forecasts of tourists arrivals are more accurate than forecasts of overnight stays.Key words: forecasting; time series models; neural networks; tourism demand; Catalonia.JEL classification: C53; C42; C45; L83. IntroductionMany stationary phenomena can be approximated by linear time series models. Nevertheless, it is generally believed that the nonlinear methods outperform the linear methods in modelling economic behaviour. Artificial intelligence techniques have become an essential tool for economic modelling and forecasting, as they are far better able to handle nonlinear behaviour. Neural networks have been applied in many areas, but only recently for tourism demand forecasting. Tourism data is characterized by strong seasonal patterns and volatility, thus original series require significant preprocessing in order to be used with forecasting purposes. While eliminating the existing outliers and adjusting the seasonal component of the series, this filtering process ends up conditioning the forecasting performance of the models. Therefore, tourism demand is a particularly interesting field in which to analyze the effects of data pre-preprocessing on forecast accuracy and to compare the forecasting performance of neural networks relative to that of time series models.There has been a growing interest in tourism research over the past decades. Some of the reasons for this increase in the number of studies of tourism demand modelling and forecasting are: the constant growth of world tourism, the utilization of more advanced forecasting techniques in tourism research and the requirement for more accurate forecasts of tourism demand at the desti...

This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results. We find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long-term forecasting.

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

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: support vector regression, Gaussian process regression and neural network models. We use an autoregressive moving average model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.Peer ReviewedPostprint (author's final draft

In this study we present a geometric approach to proxy economic uncertainty. We design a positional indicator of disagreement among survey-based agents' expectations about the state of the economy. Previous dispersion-based uncertainty indicators derived from business and consumer surveys exclusively make use of the two extreme pieces of information: the percentage of respondents expecting a variable to rise and to fall. With the aim of also incorporating the information coming from the share of respondents expecting a variable to remain constant, we propose a geometrical framework and use a barycentric coordinate system to generate a measure of disagreement, referred to as a discrepancy indicator. We assess its performance both empirically and experimentally by comparing it to the standard deviation of the share of positive and negative responses. When applied in sixteen European countries, we find that both time-varying metrics co-evolve in most countries for expectations about the country's overall economic situation in the present, but not in the future. Additionally, we obtain their simulated sampling distributions and we find that the proposed indicator gravitates uniformly towards the three vertices of the simplex representing the three answering categories, as opposed to the standard deviation, which tends to overestimate the level of uncertainty as a result of ignoring the no-change responses. Consequently, we find evidence that the information coming from agents expecting a variable to remain constant has an effect on the measurement of disagreement.

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Abstract. Trust, rather than being simply a resource for establishing collaborative relationships between organizations, is an essential component of their constitution. At base, trust involves interpersonal relations of a specific type. These are relations where there is sufficient probability that a person or organization with whom one is in contact will perform an action that is beneficial, or at least not detrimental, for one to consider engaging in some form of cooperation with this person or organization in the future. Trust establishes situations in which participants in collaboration have a long-term and recurrent relationship. Where trust exists, organizations are more willing to collaborate with other organizations on a more reciprocal basis. Trust is especially important when collaboration takes place between competitors because the risk of opportunistic behaviour is higher. Where organizations share resources and information openly with other participants they will seek to reduce opportunistic behaviour through the mutual understanding and goodwill of parties. However, trust is not static; it is a dynamic process that evolves according to the development of the relationship, as one in which the more long-term the relationship, the greater the trust. In this paper, we will present the impact of trust on business networks and examine how their members developed knowledge through networking.

In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to economic growth. Combining symbolic regression with genetic programming we generate two survey-based indicators: a perceptions index, using agents’assessments about the present, and an expectations index with their expectations about the future. In order to find the optimal combination of both indexes that best replicates the evolution of economic activity in each country we use a portfolio management procedure known as index tracking. By means of a generalized reduced gradient algorithm we derive the relative weights of both indexes. In most economies, the survey-based predictions generated with the composite indicator outperform the benchmark model for one-quarter ahead forecasts, although these improvements are only significant in Austria, Belgium and Portugal.Peer ReviewedPostprint (author's final draft