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CO2RBFN for Short and Medium Term Forecasting of the Extra-Virgin Olive Oil Price

  • M. D. Pérez-Godoy
  • P. Pérez-Recuerda
  • María Pilar Frías
  • A. J. Rivera
  • C. J. Carmona
  • Manuel Parras
Part of the Studies in Computational Intelligence book series (SCI, volume 284)

Abstract

In this paper an adaptation of CO2RBFN, evolutionary COoperative- COmpetitive 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 whole network. 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 are calculated. 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.

Keywords

Radial Basis Function ARIMA Model Conjugate Gradient Algorithm Time Series Forecast Time Series Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • M. D. Pérez-Godoy
    • 1
  • P. Pérez-Recuerda
    • 1
  • María Pilar Frías
    • 2
  • A. J. Rivera
    • 1
  • C. J. Carmona
    • 1
  • Manuel Parras
    • 3
  1. 1.Department of InformaticsUniversity of Jaén 
  2. 2.Department of Statistics and Operation ResearchUniversity of Jaén 
  3. 3.Department of MarketingUniversity of Jaén 

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