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An Increasing Hybrid Morphological-Linear Perceptron with Evolutionary Learning and Phase Correction for Financial Time Series Forecasting

  • Ricardo de A. Araújo
  • Peter Sussner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

In this paper we present a suitable model to solve the financial time series forecasting problem, called increasing hybrid morphological-linear perceptron (IHMP). An evolutionary training algorithm is presented to design the IHMP (learning process), using a modified genetic algorithm (MGA). The learning process includes an automatic phase correction step that is geared at eliminating the time phase distortions that typically occur in financial time series forecasting. Furthermore, we compare the proposed IHMP with other neural and statistical models using two complex nonlinear problems of financial forecasting.

Keywords

Lattice Theory Minimax Algebra Morphological Neural Networks Genetic Algorithms Financial Time Series Forecasting 

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References

  1. 1.
    Pessoa, L.F.C., Maragos, P.: Neural networks with hybrid morphological rank linear nodes: a unifying framework with applications to handwritten character recognition. Pattern Recognition 33, 945–960 (2000)CrossRefGoogle Scholar
  2. 2.
    Gader, P.D., Khabou, M.A., Koldobsky, A.: Morphological regularization neural networks. Pattern Recognition, Special Issue on Mathematical Morphology and Its Applications 33(6), 935–945 (2000)Google Scholar
  3. 3.
    Khabou, M.A., Gader, P.D., Keller, J.M.: LADAR target detection using morphological shared-weight neural networks. Machine Vision and Applications 11(6), 300–305 (2000)CrossRefGoogle Scholar
  4. 4.
    Sussner, P., Esmi, E.L.: Introduction to morphological perceptrons with competitive learning. In: Proceedings of the International Joint Conference on Neural Networks, Atlanta, GA, pp. 3024–3031 (2009)Google Scholar
  5. 5.
    Sussner, P., Esmi, E.L.: Morphological perceptrons with competitive learning: Lattice-theoretical framework and constructive learning algorithm. Information Sciences (2009) (accepted for publication)Google Scholar
  6. 6.
    Serra, J.: Image Analysis and Mathematical Morphology, Theoretical Advances, vol. 2. Academic Press, New York (1988)Google Scholar
  7. 7.
    Ronse, C.: Why mathematical morphology needs complete lattices. Signal Processing 21(2), 129–154 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Heijmans, H.J.A.M.: Morphological Image Operators. Academic Press, New York (1994)zbMATHGoogle Scholar
  9. 9.
    Matheron, G.: Random Sets and Integral Geometry. Wiley, New York (1975)zbMATHGoogle Scholar
  10. 10.
    Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)zbMATHGoogle Scholar
  11. 11.
    Sussner, P., Esmi, E.L.: Constructive morphological neural networks: some theoretical aspects and experimental results in classification. In: Kacprzyk, J. (ed.) Constructive Neural Networks. Studies in Computational Intelligence. Springer, Heidelberg (2009)Google Scholar
  12. 12.
    de A. Araújo, R., Madeiro, F., de Sousa, R.P., Pessoa, L.F.C., Ferreira, T.A.E.: An evolutionary morphological approach for financial time series forecasting. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2467–2474 (2006)Google Scholar
  13. 13.
    de A. Araújo, R., Ferreira, T.A.E.: An intelligent hybrid morphological-rank-linear method for financial time series prediction. Neurocomputing 72(10-12), 2507–2524 (2009)CrossRefGoogle Scholar
  14. 14.
    de A. Araújo, R., Ferreira, T.A.E.: A morphological-rank-linear evolutionary method for stock market prediction. Information Sciences (in Press, 2010)Google Scholar
  15. 15.
    Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14, 35–62 (1998)CrossRefGoogle Scholar
  16. 16.
    Sitte, R., Sitte, J.: Neural networks approach to the random walk dilemma of financial time series. Applied Intelligence 16(3), 163–171 (2002)zbMATHCrossRefGoogle Scholar
  17. 17.
    Zhang, G.P., Kline, D.M.: Quarterly time-series forecasting with neural networks. IEEE Transactions on Neural Networks 18(6), 1800–1814 (2007)CrossRefGoogle Scholar
  18. 18.
    Banon, G.J.F., Barrera, J.: Decomposition of mappings between complete lattices by mathematical morphology, part 1. general lattices. Signal Processing 30(3), 299–327 (1993)zbMATHCrossRefGoogle Scholar
  19. 19.
    Haykin, S.: Neural networks: A comprehensive foundation. Prentice Hall, New Jersey (1998)Google Scholar
  20. 20.
    Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.: Tuning of the structure and parameters of the neural network using an improved genetic algorithm. IEEE Transactions on Neural Networks 14(1), 79–88 (2003)CrossRefGoogle Scholar
  21. 21.
    Birkhoff, G.: Lattice Theory, 3rd edn. American Mathematical Society, Providence (1993)Google Scholar
  22. 22.
    Prechelt, L.: Proben1: A set of neural network benchmark problems and benchmarking rules. Technical Report 21/94 (1994)Google Scholar
  23. 23.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control, 3rd edn. Prentice Hall, New Jersey (1994)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ricardo de A. Araújo
    • 1
  • Peter Sussner
    • 2
  1. 1.Information Technology Department[gm]2 Intelligent SystemsBrazil
  2. 2.Department of Applied MathematicsUniversity of CampinasBrazil

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