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Regularized Dynamic Self Organized Neural Network Inspired by the Immune Algorithm for Financial Time Series Prediction

  • Haya Al-Askar
  • Abir Jaafar Hussain
  • Dhiya Al-Jumeily
  • Naeem Radi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8590)

Abstract

A novel type of recurrent neural network, the regularized Dynamic Self Organised Neural Network Inspired by the Immune Algorithm, is presented. The Regularization technique is used with the Dynamic self-organized multilayer perceptrons network that is inspired by the immune algorithm. The regularization has been addressed to improve the generalization and to solve the over-fitting problem. The results of an average 30 simulations generated from ten stationary signals are demonstrates. The results of the proposed network were compared with the regularized multilayer neural networks and the regularized self organized neural network inspired by the immune algorithm. The simulation results indicated that the proposed network showed better values in terms of the annualized return in comparison to the benchmarked networks.

Keywords

Dynamic neural network exchange rate time series and financial time series prediction 

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References

  1. 1.
    Kamruzzaman, J.: ANN-Based Forecasting of Foreign Currency Exchange Rates. Neural Information Processing - Letters and Reviews 3(2), 49–58 (2004)Google Scholar
  2. 2.
    Espinoza, R., Lombardi, M.J., Fornari, F.: The role of financial variableS in PredicTing economic activity. ECB Working Paper Series, vol. (1108). Frankfurt am Main, Germany (2009)Google Scholar
  3. 3.
    Leondes, C.T.: Intelligent Knowledge-Based Systems: Business and Technology in the New Millennium illustrate. Springer (2010)Google Scholar
  4. 4.
    Kamruzzaman, J., Sarker, R.: Forecasting of currency exchange rates using ANN: a case study. In: Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, vol. 1, pp. 793–797. IEEE, Nanjing (2003)CrossRefGoogle Scholar
  5. 5.
    Krollner, B.: Risk Management in the Australian Stockmarket using Artificial Neural networks, PhD Thesis (2011)Google Scholar
  6. 6.
    Tan, T.Z., Quek, C., Ng, G.S.: Brain-inspired Genetic Complementary Learning for Stock Market Prediction. In: IEEE Congress on Evolutionary Computation, vol. 3, pp. 2653–2660 (2005)Google Scholar
  7. 7.
    Ahmadifard, M., Sadenejad, F., Mohammadi, I., Aramesh, K.: Forecasting stock market return using ANFIS: the case of Tehran Stock Exchange. International Journal of Advanced Studies in Humanities and Social Science 1(5), 452–459 (2013)Google Scholar
  8. 8.
    Mahdi, A.: The Application of Neural Network inFinancial Time Series Analysis and Prediction Using Immune System. Liverpool John Moores University (2010)Google Scholar
  9. 9.
    Mahdi, A., Hussain, A., Al-Jumeily, D.: The Prediction of Non-Stationary Physical Time Series Using the Application of Regularization Technique in Self-organised Multilayer Perceptrons Inspired by the Immune Algorithm. E-systems Eng., 213–218 (September 2010)Google Scholar
  10. 10.
    Jordan, M.I.: Attractor dynamics and parallelism in a connectionist sequential machine. In: Artificial Neural Networks, NJ, USA, pp. 112–127. IEEE Press, Piscataway (1990)Google Scholar
  11. 11.
    Voegtlin, T.: Recursive self-organizing maps. Neural Netw. 15(8-9), 79–91 (2002)CrossRefGoogle Scholar
  12. 12.
    Widyanto, M.R., Nobuhara, H., Kawamoto, K., Hirota, K., Kusumoputro, B.: Improving recognition and generalization capability of back-propagation NN using self-organized network inspired by immune algorithm. Appl. Soft Comput. 6, 72–84 (2005)CrossRefGoogle Scholar
  13. 13.
    Bishop, C.M.: Neural Networks for Pattern Recognition, Cambridge, UK (1995)Google Scholar
  14. 14.
    Thomason, M.: The practitioner method and tools. J. Comput. Intell. Financ. 7(3), 36–45 (1999)Google Scholar
  15. 15.
    Cao, L.J., Tay, F.E.H.: Financial Time Series Forecasting. IEEE Trans. Neural Networks 14(6), 1506–1518 (2003)CrossRefGoogle Scholar
  16. 16.
    Dunis, C.L., Williams, M.: Applications of Advanced Regression Analysis for Trading and Investment. John Wiley & Sons, Ltd. (2003)Google Scholar
  17. 17.
    Cao, L.J., Tay, F.E.H.: Support Vector Machine with Adaptive Parameters in Financial time Series Forecasting. IEEE Trans. Neural Networks 14(6), 1506–1518 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haya Al-Askar
    • 1
  • Abir Jaafar Hussain
    • 1
  • Dhiya Al-Jumeily
    • 1
  • Naeem Radi
    • 2
  1. 1.Liverpool John Moores UniversityLiverpoolUK
  2. 2.Al-Khawarizmi International CollegeAbu DhabiUnited Arab Emirates

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