A new evolutionary approach for time series forecasting

Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Volumn , Issue , 2007, Pages 616-623

  Ferreira, Tiago A E ^a   Vasconcelos, Germano C ^a   Adeodato, Paulo J L ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDED SYSTEMS; GENETIC ALGORITHMS; MULTILAYER NEURAL NETWORKS; STATISTICAL TESTS; TIME SERIES ANALYSIS;

TIME LAGS; TIME PHASE DISTORTIONS; TRAINING ALGORITHMS;

TECHNOLOGICAL FORECASTING;

EID: 34548771933     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIDM.2007.368933     Document Type: Conference Paper

References (25)

1. 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.
2. T. Ozaki. Nonlinear Time Series Models and Dynamical Systems, ser. Hand Books of Statistics, E.J. Hannan and P. R. K. eds, Eds. Amsterdam: Noth-Holland, 1985, vol. 5.
3. M. B. Priestley, Non-Linear and Non-Stacionary time Series Analysis. London: Academic Press. 1988.
4. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, The MIT Press, 1986, vol. 1: Foundations.
5. G. Zhang, B. E. Patuwo, and M.Y. Hu, "Forecasting with artificial neural networks: The state of the art," International Journal of Forecating, vol. 14, pp. 35-62, 1998.
6. X. Yao, "Evolving artificial neural networks," Proceeding of IEEE, vol. 87, no. 9, pp. 1423-1447, September 1999.
7. K. O. Stanley and R. Miikkulainen, "Evolving neral network though augmenting topologies, "Evalutionary COmputation, Vol. 10, no. 2, pp. 99-127, 2002.
8. D. A. Miller, R. Arquello, and G.W.Greenwood, "Evolving artificial neural network structures: Experimental results for biologivally inspired adaptive mutation," in Proceedings of CEC2004 - Congress on Evolutionary Computation, vol. 2, IEEE, 2004, pp. 2114-2119.
9. F. H. F. Leung, H. K. Lam, S. H. Ling, and P. K. S. Tam, "Tuning of the structure and parametrs of the neural networks using an improved genetic algorthum," IEEE Transaction on Neural Networks, vol. 14, no, 1, pp. 79-88, January 2003.
10. M. M. Islan, X. Yao, K. Muraz, "A construtive algorithm for training cooperative neural networks ensembles," IEEE Transactions on Neural Networks, vol. 14, no. 4, pp. 820-834, july 2003.
11. J. Binner, G. Kendall, and A. new application to divisia money," Advances in Econometrics, vol. 19, pp. 127-143, 2004.
12. A. S. Andreou, E. F. Georgopolulos, and S. D. Likothanassis, "Exchange-rates forecasting: A hybrid algorithm based on genetically optimized adaptive neural networks," Computational Economics, vol. 20, no. 3, pp. 191-210, 2002.
13. M. Matilla-García and C. Argüello, "A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the spanish stock market," Applied Economics Letters, vol. 12, no, 5, pp. 303-308, April 2005.
14. S. Haykins, Neural Networks: A Comprehensive Foundation, 2nd ed. Princete Hall. 1998.
15. F. Takens, "Detecting strange attractor in turbulence," in Dynamical Systems and Turbulence, Eds., vol. 898, New York: Springer-Verlage, 1980, pp. 366-381.
16. H. Pi and C. Peterson, "Finding the embedding demension and variable dependences in time series," Neural Computation, vol 6, pp. 509-520, 1994.
17. N. Tanaka, H. Okamoto, and M. Naito, "Estimating the active dimension of the dynamics in a time series based on a information criterion," Physica D, vol. 158, pp. 19-31, 2001.
18. 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.
19. T. C. Mills, The Econometric Modelling of Financial Time Series, Cambridge: Cambridge University Press, 2003.
20. 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.
21. M. P. Clementes and D. F. Hendry, "On the limitations of comparing mean squared forecast erros," Journal of Forecasting, vol. 12, no. 8, pp. 617-637, 1993.
22. D. B. Murray, "Forecasting a chaotic time series using an improved metric for embedding space," Physica D, vol. 68, pp. 318-325, 1993.
23. N. Teru and H. K. V. Dijk, "Combined forecasts form linear and nonlinear time series models," International Journal of Forecasting, vol. 18, pp. 421-438, 2002.
24. T. Bollerslev, "Generalized autoregressive condicional heteroskedasticity," Journal of Econometrics, vol. 31, pp. 307-327, 1986.
25. R. F. Engle. "Autoregressive conditional heteroskedasticity with estimates of the variance of uk onflation," Econometrica, vol. 50, pp. 987-1008, 1982.