CO2RBFN for short and medium term forecasting of the extra-virgin olive oil price

Studies in Computational Intelligence

Volumn 284, Issue , 2010, Pages 113-125

  Perez Godoy M D ^a   Perez Recuerda P ^a   Frías, María Pilar ^a   Rivera, A J ^a   Carmona, C J ^a   Parras, Manuel ^a  

^a UNIVERSITY OF JAÉN   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77951676441     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-12538-6_10     Document Type: Conference Paper

References (33)

1. 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)
2. 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
3. Bäck, T., Hammel, U., Schwefel, H.: Evolutionary computation: comments on the history and current state. IEEE Transaction Evolutive Compututation 1(1), 3-17 (1997)
4. Box, G., Jenkins, G.: Time series analysis: forecasting and control, revised edn. Holden Day, San Francisco (1976)
5. Broomhead, D., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex System 2, 321-355 (1998)
6. 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)
7. 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)
8. 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)
9. 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)
10. 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)
11. 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)
12. Du, H., Zhang, N.: Time series prediction using evolving radial basis function networks with new encoding scheme. Neurocomputing 71(7-9), 1388-1400 (2008)
13. Franses, P.H., van Dijk, D.: Non-linear time series models in empirical finance. Cambridge University Press, Cambridge (2000)
14. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998)
15. 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)
16. Howard, L., D'Angelo, D.: The GA-P: A Genetic Algorithm and Genetic Programming Hybrid. IEEE Expert, 11-15 (1995)
17. Jang, J.R.: ANFIS: Adaptative-Network-based Fuzzy Inference System. IEEE Trans. Systems, Man and Cybernetics 23(3), 665-685 (1993)
18. 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)
19. 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)
20. Mandani, E., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1-13 (1975)
21. 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
22. Moller, F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525-533 (1990)
23. Park, J., Sandberg, I.: Universal approximation using radial-basis function networks. Neural Comput. 3, 246-257 (1991)
24. 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)
25. 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
26. 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)
27. 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)
28. 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)
29. Sheta, A.F., De Jong, K.: Time-series forecasting using GA-tuned radial basis functions. Information Sciencie 133, 221-228 (2001)
30. 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)
31. 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)
32. Widrow, B., Lehr, M.A.: 30 Years of adaptive neural networks: perceptron, madaline and backpropagation. Proceedings of the IEEE 78(9), 1415-1442 (1990)
33. Yu, T., Wilkinson, D.: A co-evolutionary fuzzy system for reservoir well logs interpretation. Evolutionary computation in practice, 199-218 (2008)