An evolutionary morphological-rank-linear approach for time series prediction

2007 IEEE Congress on Evolutionary Computation, CEC 2007

Volumn , Issue , 2007, Pages 4321-4328

  Araújo, Ricardo De A ^a,b   Vasconcelos, Germano C ^b   Ferreira, Tiago A E ^b  

^a IEEE

^b FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

MORPHOLOGICAL-RANK-LINEAR (MRL); TIME SERIES PREDICTION;

CONVERGENCE OF NUMERICAL METHODS; MATHEMATICAL MODELS; MORPHOLOGY; PARAMETER ESTIMATION; TIME SERIES ANALYSIS;

GENETIC ALGORITHMS;

EID: 77957687704     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2007.4425035     Document Type: Conference Paper

References (26)

1. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 3rd ed. New Jersey: Prentice Hall, 1994.
2. T. S. Rao and M. M. Gabr, Introduction to Bispectral Analysis and Bilinear Time Series Models, ser. Lecture Notes in Statistics. Berlin: Springer, 1984, vol. 24.
3. T. Ozaki, Nonlinear Time Series Models and Dynamical Systems, ser. Hand Book of Statistics, E. J. Hannan and P. R. K. eds, Eds. Amsterdam: Noth-Holland, 1985, vol. 5.
4. M. B. Priestley, Non-Linear and Non-Stacionary Time Series Analysis. Academic Press, 1988.
5. D. E. Rumelhart and J. L. McCleland, Parallel Distributed Processing, Explorations in the Microstructure of Cognition. MIT Press, 1987, vol. 1 & 2.
6. M. P. Clements, P. H. Franses, and N. R. Swanson, "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, vol. 20, no. 2, pp. 169-183, 2004.
7. G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," International Journal of Forecasting, vol. 14, pp. 35-62, 1998.
8. F. H. F. Leung, H. K. Lam, S. H. Ling, and P. K. S. Tam, "Tuning of the structure and parameters of the neural network using an improved genetic algorithm," IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 79-88, January 2003.
9. C. T. Chan, C. H. Chuek, D. P. Mital, and T. T. Huat, "Time series modelling and forecasting using genetic algorithms," in First International Conference on Knowledge-Based Intelligent Electronic Systems, L. C. Jain, Ed., Adelaide, 1997, pp. 260-268.
10. S. H. Ling, H. K. Lam, F. H. F. Leung, and Y. S. Lee, "A genetic algorithm based neural-tuned neural network," in The 29th Annual Conference of the IEEE Industrial Eletronics Society - IECON'03, vol. 3, 2003, pp. 2423-2428.
11. T. A. E. Ferreira, G. C. Vasconcelos, and P. J. L. Adeodato, "A hybrid intelligence system approach for improving the prediction of real world time series," in IEEE Proceedings of the Congress on Evolutionary Computation, vol. 1. Portland, Oregon: IEEE, 2004, pp. 736-743.
12. T. A. E. Ferreira, "A new hybrid intelligent methodology for time series forecasting," Ph.D. dissertation, Federal University of Pernambuco, 2006.
13. R. A. Araújo, P. S. G. Mattos, G. G. Petry, and T. A. E. Ferreira, "An intelligent evolutionary hybrid approach for time series forecasting," in Proceedings of the Brazilian Computation Society Congress, Campo Grande, MS, Brazil, 2006.
14. P. Maragos, "A representation theory for morphological image and signal processing," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 11, pp. 586-599, 1989.
15. J. Serra, Image analysis and mathematical morphology. London: Academic Press, 1982.
16. P. Salembier, "Structuring element adaptation for morphological filters," Journal for Visual Communication and Image Representation, vol. 3, no. 2, pp. 115-136, Jun. 1992.
17. P. Salembier, "Adaptive rank order based filters," Signal Process., vol. 27, no. 1, pp. 1-25, 1992.
18. L. F. C. Pessoa and P. Maragos, "MRL-filters: A general class of nonlinear systems and their optimal design for image processing," IEEE Transactions on Image Processing, vol. 7, pp. 966-978, 1998.
19. R. A. Araújo, F. Madeiro, R. P. Sousa, L. F. C. Pessoa, and T. A. E. Ferreira, "An evolutionary morphological approach for financial time series forecasting," in Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, Canada, 2006.
20. 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.
21. N. R. Harvey and S. Marshall, "The use of genetic algorithms in morphological filter design," Signal Processing Image Communication, vol. 8, pp. 55-71, 1996.
22. L. Prechelt, "Proben1: A set of neural network benchmark problems and benchmarking rules," Tech. Rep. 21/94, 1994. [Online]. Available: citeseer.ist.psu.edu/prechelt94proben.html
23. M. P. Clements and D. F. Hendry, "On the limitations of comparing mean square forecast errors," Journal of Forecasting, vol. 12, no. 8, pp. 617-637, Dec. 1993.
24. T. C. Mills, The Econometric Modelling of Financial Time Series. Cambridge: Cambridge University Press, 2003.
25. M. Hagan and M. Menhaj, "Training feedforward networks with the marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989-993, November 1994.
26. 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.