Abstract
In this paper we present a summary of the application of CO2RBFN, a evolutionary cooperative-competitive algorithm for Radial Basis Function Networks design, to the medium-term forecasting of the extra-virgen olive price, carry out by the SIMIDAT research group. The forecast is about the price at source of the extra-virgin olive oil six months ahead. The influential of the feature selection algorithms over the forecasting of the extra-virgin olive oil price has been analysed in this study and the results obtained with CO2RBFN have been compared with those obtained by different soft computing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J. of Mult.-Valued Logic & Soft Computing 17, 255–287 (2011)
Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques - part ii: Soft computing methods. Expert Systems with Applications 36(3,Part 2), 5932–5941 (2009)
García, F., García, M., Melián, B., Moreno, J.A., Marcos, J.: Solving feature subset selection problem by a parallel scatter search. European Journal of Operational Research 169, 477–489 (2006)
Ghost, J., Deuser, L., Beck, S.: A neural network based hybrid system for detection, characterization and classification of short-duration oceanic signals. IEEE Journal of Ocean Enginering 17(4), 351–363 (1992)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Gütlein, M., Frank, E., Hall, M., Karwath, A.: Large scale attribute selection using wrappers. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (1999)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11(1) (2009)
Hall, M., Smith, L.A.: Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper. In: Twelfth International FLAIRS Conference, MIT Press, AAAI/ (1999)
Moller, F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1990)
Pérez-Godoy, M.D., Pérez, P., Rivera, A.J., del Jesus, M.J., Carmona, C.J., Frías, M.P., Parras, M.: co 2 rbfn for short-term forecasting of the extra virgin olive oil price in the spanish market. International Journal of Hybrid Intelligent Systems 7(1), 75–87 (2010)
Rivera, A.J., Pérez-Recuerda, P., Pérez-Godoy, M.D., del Jesus, M.J., Frías, M.P., Parras, M.: A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods. Applied Intelligent 34(3), 331–346 (2011)
Sánchez, L., Couso, I.: Fuzzy random variables-based modeling with ga-p algorithms. In: Bouchon, B., Yager, R.R., Zadeh, L. (eds.) Information, Uncertainty and Fusion, pp. 245–256 (2000)
Widrow, B., Lehr, M.A.: 30 years of adaptive neural networks: perceptron, madaline and backpropagation. Proceedings of the IEEE 78(9), 1415–1442 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rivera, A.J., Pérez-Godoy, M.D., del Jesus, M.J., Pérez-Recuerda, P., Frías, M.P., Parras, M. (2011). A Summary on the Study of the Medium-Term Forecasting of the Extra-Virgen Olive Oil Price. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-25274-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25273-0
Online ISBN: 978-3-642-25274-7
eBook Packages: Computer ScienceComputer Science (R0)