CO$^2$RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market

Authors:
M. D. Pérez-Godoy;P. Pérez;A. J. Rivera;M. J. del Jesus;C. J. Carmona;M. P. Frías;M. Parras
Affiliations:
(Correspd. E-mail: lperez@ujaen.es) Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain;Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain;Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain;Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain;Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain;Department of Statistics and Operation Research, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain;Department of Marketing, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain
Venue:
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Year:
2010

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Abstract

This paper presents the adaptation of CO$^2$RBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra virgin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire population is responsible for the final solution. In order to calculate the application probability of the evolutive operators over a certain Radial Basis Function, a Fuzzy Rule Based System has been used. The olive oil time series have been analyzed using CO$^2$RBFN. The results obtained have been compared with Auto-Regressive Integrated Moving Average (ARIMA) models and other data mining methods such as a fuzzy system developed with a GA-P algorithm, a multilayer perceptron trained with a conjugate gradient algorithm, and a radial basis function network trained using an LMS algorithm. The experimentation shows the high efficiency achieved by these methods, especially the data mining methods, which have slightly outperformed the ARIMA methodology.