Intelligent systems in long-term forecasting of the extra-virgin olive oil price in the Spanish market

Authors:
María Dolores Pérez-Godoy;Pedro Pérez;Antonio Jesús Rivera;María José del Jesús;María Pilar Frías;Manuel Parras
Affiliations:
Department of Computer Science, University of Jaén, Spain;Department of Computer Science, University of Jaén, Spain;Department of Computer Science, University of Jaén, Spain;Department of Computer Science, University of Jaén, Spain;Department of Statistics and Operation Research, University of Jaén, Spain;Department of Marketing, University of Jaén, Spain
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Year:
2010

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Abstract

In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits of the implied organizations must be highlighted. In this paper an adaptation of CO2RBFN, evolutionary COoperative-COmpetitive algorithm for Radial Basis Function Networks design, applied to the long-term prediction of the extra-virgin olive oil price is presented. This long-term horizon has been fixed to six months. The results of CO2RBFN have been compared with other data mining methods, typically used in time series forecasting, such as other neural networks models, a support vector machine method and a fuzzy system.