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Article
Publication date: 12 October 2015

Oscar Claveria, Enric Monte and Salvador Torra

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…

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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.

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.

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.

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 has been developed to improve forecasting accuracy, providing professionals with an innovative and practical forecasting approach.

Details

International Journal of Contemporary Hospitality Management, vol. 27 no. 7
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 1 March 2003

HIPÒLIT TORRÓ, VICENTE MENEU and ENRIC VALOR

The authors employ single‐factor models to estimate daily temperature variations for the valuation of weather derivatives. Classical financial models are adapted to fit…

217

Abstract

The authors employ single‐factor models to estimate daily temperature variations for the valuation of weather derivatives. Classical financial models are adapted to fit temperature seasonality to a time series. As an example, Monte Carlo simulations of heating and cooling degree‐days are used as the underlying for weather derivatives that reference temperatures in regions of Spain. The article also discusses potential applications to hedging energy‐related risks.

Details

The Journal of Risk Finance, vol. 4 no. 4
Type: Research Article
ISSN: 1526-5943

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