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.
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References
Alcalá-Fdez, J., Sánchez, L., García, S., Del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J.C., Herrera, F.: KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems. Soft Computing 13(3), 307–318 (2009)
Azadeh, A., Saberi, M., Ghaderi, S.F., Gitiforouz, A., Ebrahimipour, V.: Improved estimation of electricity demand function by integration of fuzzy system and data mining approach. Energy Conversion and Management (2008) doi:10.1016/j.enconman.2008.02.021
Bäck, T., Hammel, U., Schwefel, H.: Evolutionary computation: comments on the history and current state. IEEE Transaction Evolutive Compututation 1(1), 3–17 (1997)
Box, G., Jenkins, G.: Time series analysis: forecasting and control, revised edn. Holden Day, San Francisco (1976)
Broomhead, D., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex System 2, 321–355 (1998)
Buchtala, O., Klimek, M., Sick, B.: Evolutionary optimization of radial basis function classifiers for data miningapplications. IEEE Transactions on Systems, Man and Cybernetics Part B 35(5), 928–947 (2005)
Chen, C., Wu, Y., Luk, B.L.: Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Transaction Neural Networks 10(5), 1239–1243 (1999)
Co, H.C., Boosarawongse, R.: Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers and Industrial Engineering 53(4), 610–627 (2007)
Fan, R.E., Chen, P.H., Lin, C.J.: Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889–1918 (2005)
Ghost, J., Deuser, L., Beck, S.: A neural network based hybrid system for detection, characterization and classification of short-duration oceanic signals. IEEE Jl. Of Ocean Enginering 17(4), 351–363 (1992)
Goldberg, D., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Grefenstette (ed.) Proc. Second International Conference on Genetic Algorithms, pp. 41–49. Lawrence Erlbaum Associates, Mahwah (1987)
Du, H., Zhang, N.: Time series prediction using evolving radial basis function networks with new encoding scheme. Neurocomputing 71(7-9), 1388–1400 (2008)
Franses, P.H., van Dijk, D.: Non-linear time series models in empirical finance. Cambridge University Press, Cambridge (2000)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998)
Hobbs, B.F., Helman, U., Jitprapaikulsarn, S., Konda, S., Maratukulam, D.: Artificial neural networks for short-term energy forecasting: Accuracy and economic value. Neurocomputing 23(1-3), 71–84 (1998)
Howard, L., D’Angelo, D.: The GA-P: A Genetic Algorithm and Genetic Programming Hybrid. IEEE Expert, 11–15 (1995)
Jang, J.R.: ANFIS: Adaptative-Network-based Fuzzy Inference System. IEEE Trans. Systems, Man and Cybernetics 23(3), 665–685 (1993)
Khashei, M., Reza Hejazi, S., Bijari, M.: A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets and Systems 159(7), 769–786 (2008)
Liu, J., McKenna, T.M., Gribok, A., Beidleman, B.A., Tharion, W.J., Reifman, J.: A fuzzy logic algorithm to assign confidence levels to heart and respiratory rate time series. Physiological Measurement 29(1), 81–94 (2008)
Mandani, E., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)
Meng, K., Dong, Z.Y., Wong, K.P.: Self-adaptive radial basis function neural network for short-term electricity price forecasting. IET Generation, Transmission and Distribution 3(4), 325–335
Moller, F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1990)
Park, J., Sandberg, I.: Universal approximation using radial-basis function networks. Neural Comput. 3, 246–257 (1991)
Pérez, P., Frías, M.P., Pérez-Godoy, M.D., Rivera, A.J., del Jesus, M.J., Parras, M., Torres, F.J.: An study on data mining methods for short-term forecasting of the extra virgin olive oil price in the Spanish market. In: Proceeding of the International Conference On Hybrid Intelligetn Systems, pp. 943–946 (2008)
Pérez-Godoy, M.D., Rivera, A.J., Berlanga, F.J., Jesús, M.J.: CO2RBFN: an evolutionary cooperative-competitive RBFN design algorithm for classification problems. Soft Computing (in press) (2009) doi: 10.1007/s00500-009-0488-z
Pino, R., Parreno, J., Gomez, A., Priore, P.: Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Engineering Applications of Artificial Intelligence 21(1), 53–62 (2008)
Rivas, V., Merelo, J.J., Castillo, P., Arenas, M.G., Castellano, J.G.: Evolving RBF neural networks for time-series forecasting with EvRBF. Information Science 165, 207–220 (2004)
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)
Sheta, A.F., De Jong, K.: Time-series forecasting using GA-tuned radial basis functions. Information Sciencie 133, 221–228 (2001)
Ture, M., Kurt, I.: Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Systems with Applications 31(1), 41–46 (2006)
Whitehead, B., Choate, T.: Cooperative-competitive genetic evolution of Radial Basis Function centers and widths for time series prediction. IEEE Trans. on Neural Networks 7(4), 869–880 (1996)
Widrow, B., Lehr, M.A.: 30 Years of adaptive neural networks: perceptron, madaline and backpropagation. Proceedings of the IEEE 78(9), 1415–1442 (1990)
Yu, T., Wilkinson, D.: A co-evolutionary fuzzy system for reservoir well logs interpretation. Evolutionary computation in practice, 199–218 (2008)
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Pérez-Godoy, M.D., Pérez-Recuerda, P., Frías, M.P., Rivera, A.J., Carmona, C.J., Parras, M. (2010). CO2RBFN for Short and Medium Term Forecasting of the Extra-Virgin Olive Oil Price. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_10
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DOI: https://doi.org/10.1007/978-3-642-12538-6_10
Publisher Name: Springer, Berlin, Heidelberg
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