CO^2RBFN for Short and Medium Term Forecasting of the Extra-Virgin Olive Oil Price

• M.D. Pérez-Godoy ^[1] ; P. Pérez-Recuerda ^[1] ; María Pilar Frías ^[1] ; A.J. Rivera ^[1] ; C.J. Carmona ^[1] ; Manuel Parras ^[1]
  1. [1] Universidad de Jaén

     Universidad de Jaén

     Jaén, España

     
• Localización: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) / coord. por Juan R. González, David Alejandro Pelta Mochcovsky, Carlos Cruz, Germán Terrazas, Natalio Krasnogor, 2010, ISBN 978-3-642-12537-9, págs. 113-126
• Idioma: inglés
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• Resumen

  • In this paper an adaptation of CO2RBFN, evolutionary COoperativeCOmpetitive algorithm for Radial Basis Function Networks design, applied to the prediction of the extra-virgin olive oil price is presented. In this algorithm each individual represents a neuron or Radial Basis Function and the population, the wholenetwork. Individuals compite for survival but must cooperate to built the definite solution. The forecasting of the extra-virgin olive oil price is addressed as a time series forecasting problem. In the experimentation medium-term predictions are obtained for first time with these data. Also short-term predictions with new data arecalculated. The results of CO2RBFN have been compared with the traditional statistic forecasting Auto-Regressive Integrated Moving Average method and other data mining methods such as other neural networks models, a support vector machine method or a fuzzy system.