An experimental study with a hybrid method for tuning neural network for time series prediction

2008 IEEE Congress on Evolutionary Computation, CEC 2008

Volumn , Issue , 2008, Pages 3435-3442

  Aranildo Jr , R L ^a   Ferreira, Tiago A E ^b   Araújo, Ricardo De A ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

^b University Federal Rural of Pernambuco   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; EVOLUTIONARY ALGORITHMS; FORECASTING; HYBRID SENSORS; IMAGE CLASSIFICATION; NEURAL NETWORKS; TUNING;

ARTIFICIAL NEURAL NETWORKS; EVOLUTIONARY STRATEGIES; EXPERIMENTAL ANALYSES; EXPERIMENTAL STUDIES; FITNESS VALUES; FORECASTING MODELS; GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES; HYBRID METHODS; LEARNING PERFORMANCES; PARAMETERS CONFIGURATIONS; PERFORMANCE MEASURES; TIME LAGS; TIME SERIES; TIME SERIES PREDICTIONS;

TIME SERIES ANALYSIS;

EID: 55849091038     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2008.4631262     Document Type: Conference Paper

References (29)

1. G. M. J. G. E. P. Box and G. C. Reinsel, Time Series Analysis: Forecasting and Control. New Jersey: Prentice Hall, 1994.
2. T. S. Rao and M. M. Gabr, "Introduction to bispectral analysis and bilinear time series models," in Lecture Notes in Statistics, vol. 24. Berlin: Springer, 1984.
3. T. Ozaki, "Nonlinear time series models and dynamical systems," in Hand Books of Statistics, vol. 5. Amsterdam: Noth-Holland: E. J. Hannan and P. R. K. eds, 1985.
4. M. B. Priestley, Non-Linear and Non-Stacionary time Series Analysis. London: Academic Press, 1988.
5. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing. London: The MIT Press, 1986, vol. 1: Foundations.
6. H. S. M. Ghiassi and D. K. Zimbra, "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, vol. 21, no. 2, pp. 341-362, April-June 2005.
7. L. M. de Menezes and N. Y. Nikolaev, "Forecasting with genetically programmed polynomial neural networks," International Journal of Forecasting, vol. 22, no. 2, pp. 249-265, April-June 2006.
8. T. Feo and M. Resende, "Greedy randomized adaptive search procedures," Journal of Global Optimization, vol. 6, no. 2. pp. 109-133, March 1995.
9. M. G. Resende and C. C. Ribeiro, "Greedy randomized adaptive search procedures," in Handbook of Metaheuristics, vol. 57. Springer, 2003, pp. 219-250.
10. J. R. Koza, Genetic Programming - On the Programming of Computers by Means of Natural Selection, Cambridge, Massachusetts: The MIT Press, 1998.
11. H. L. S. T. P. Leung, F.H.F.; Lam, "Tuning of the structure and parameters of a neural network using an improved genetic algorithm." Neural Networks, IEEE Transactions on, vol. 12, no. 1, pp. 79-88, Jan 2003.
12. A. E. Eiben and J. E. Smith, Introduction to Evoluionary Computing. Springer, 2003.
13. G. A. P. Ferreira, T.A.E.; Vasconcelos, "A new evolutionary approach for time series forecasting," Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on, pp. 616-623, March 1 2007-April 5 2007.
14. F. Takens, "Detecting strange attractor in turbulence," in Dynamical Systems and Turbulence, ser. Lecture Notes in Mathematics. A. Dold and B. Eckmann, Eds., vol. 898. New York: Springer-Verlag, 1980, pp. 366-381.
15. S. J. Russell and P. Norvig, Artificial Intelligence - A Modern Approach. Englewood Cliffs New Jersey 07632: Prentice Hall, 1995.
16. I. Rechenberg, Evolutionstrategie: Optimierung Technisher Systeme nach Prinzipien des Biologischen Evolution, Stuttgart: Fromman-Hozlboog Verlag, 1973.
17. G. Cybenko, "Approximation by superpositions of a sigmoidal function." Mathematical Control Signals Systems, vol. 2, pp. 303-314, 1989.
18. G. G. P. T. A. E. F. Paulo S. G. de M. Neto, Ricardo de A. Araujo and G. C. Vasconcelos, "Hybrid swarm system for time series forecasting," in VI Encontro Nacional de Inteligncia Artificial (ENIA 2007). SBC, 2007.
19. S. Haykin, Neural Networks - A Comprehensive Foundation. Pearson Education, 1999.
20. M. Hagan and M. Menhaj, "Training feedforward networks with the marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, no. 6, p. 9S9993, November 2002.
21. T. A. E. Ferreira, "A new intelligent hybrid methodology for the time serie forecasting," Ph.D. dissertation, Federal University of Pernambuco, 50732-970, Recife-PE, Brazil, Febuary 2006.
22. R. A. Araújo, G. C. Vasconcelos, and T. A. E. Ferreira, "Hybrid differential evolutionary system for financial time series forecasting," in Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, 2007.
23. F. d. S. R. P. L. F. T. de A. Araujo, R.; Madeiro, "An evolutionary morphological approach for financial time series forecasting," Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pp. 2467-2474, 16-21 July 2006.
24. T. C. Mills. The Econometric Modelling of Financial Time Series. Cambridge: Cambridge University Press, 2003.
25. N. Teru and H. K. V. Dijk, "Combined forecasts form linear and nonlinear time series models," Intenational Journal of Forecasting, no. 18, p. 421438, 2002.
26. D. B. Murray, "Forecasting a chaotic time series using an improved metric for embedding space," Physica D, no. 68, p. 318325, 1993.
27. R. Jain, The Art of Computer Systems Performance Analysis. New York: John Wiley & Sons, INC, 1991.
28. R. Sitte and J. Sitte, "Neural networks approach to the random walk dilemma of financial time series," Applied Intelligence, vol. 16, no. 3, pp. 163-171, May 2002.
29. G. C. V. T. A. E. Ferreira and P. J. L. Adeodato, "A new intelligent methodology for times series ferecasting," New Mathematics and Natural Computation, vol. 2, no. 3, 2006.