Advertisement

Physical Time Series Prediction Using Dynamic Neural Network Inspired by the Immune Algorithm

  • Abir Jaafar Hussain
  • Haya Al-Askar
  • Dhiya Al-Jumeily
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8779)

Abstract

Time series analysis is a fundamental subject that has been addressed widely in different fields. It has been exploited and used in different scientific fields for example, natural, biomedical, economic and industrial data as well as financial time series. In this paper, we consider the application of a novel neural network architecture inspired by the immune algorithm and the recurrent links for the prediction of Lorenz and earthquake time series by exploiting the inherent temporal capabilities of the recurrent neural model. The performance of this network is benchmarked against “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron network, a Jordan and an Elman neural network as well as the self organized neural network inspired by the immune algorithm. The results indicate that the inherent temporal characteristics of the recurrent links network make it extremely well suited to the processing of time series based data.

Keywords

Recurrent neural network self organised neural network and physical time series prediction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Roverso, D.: Multivariate temporal classification by windowed wavelet decomposition and recurrent neural networks. In: Proceedings of the 3rd ANS International Topical Meeting on Nuclear Plant Instrumentation, Washington, DC, USA, Control and Human-Machine Interface Technologies, pp. 527–538 (2000) Google Scholar
  2. Mirea, L., Marcu, T.: System identification using Functional-Link Neural Networks with dynamic structure. In: 15th Triennial World Congress, Barcelona, Spain (2002)Google Scholar
  3. Herrera, J.L.: Time Series Prediction Using Inductive Reasoning Techniques. Instituto de Organizacion y Control de Sistemas Industriales (1999)Google Scholar
  4. Ghazali, R., Hussain, A., Nawi, N., Mohamad, B.: Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network. Neurocomputing 72(10-12), 2359–2367 (2009)CrossRefGoogle Scholar
  5. Yümlü, S., Gürgen, F.S., Okay, N.: A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction. Pattern Recognition Letters 26(13), 2093–2103 (2005)CrossRefGoogle Scholar
  6. Mengistu, S.G., Quick, C.G., Creed, I.F.: Nutrient export from catchments on forested landscapes reveals complex nonstationary and stationary climate signals. Water Resources Research 49(6), 3863–3880 (2013), http://doi.wiley.com/10.1002/wrcr.20302 (accessed October 2013)CrossRefGoogle Scholar
  7. Aamodt, R.: Using Artificial Neural Networks to Forecast Financial Time Series. Unpublished Master’s thesis. Norwegian University of Science and Technology (2010)Google Scholar
  8. Widyanto, M.R., Nobuhara, H., Kawamoto, K., Hirota, K., Kusumoputro, B.: Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA). Applied Soft Computing 6(1), 72–84 (2005)CrossRefGoogle Scholar
  9. Jordan, M.I.: Attractor dynamics and parallelism in a connectionist sequential machine. In: Artificial Neural Networks, pp. 112–127. IEEE Press, Piscataway (1990)Google Scholar
  10. Mahdi, A.A., Hussain, A.J., 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. 2010 Developments in E-systems Engineering, pp. 213–218 (2010), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5633838 (accessed February 11, 2014)
  11. Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers. Wiley, New York (1999)Google Scholar
  12. Jordan, M.I.: Attractor dynamics and parallelism in a connectionist sequential machine. In: Artificial Neural Networks, pp. 112–127. IEEE Press, Piscataway (1990)Google Scholar
  13. Elman, L.J.: Finding Structure in Time. Cognitive Science 14, 179–211 (1990)CrossRefGoogle Scholar
  14. Al-Jumeily, D., Hussain, A., Alaskar, H.: Recurrent Neural Networks Inspired by Artificial Immune Algorithm for Time Series Prediction. In: International Joint Conference on Neural Networks, Dallas, USA, August 3-9 (2013) ISBN: 978-1- 4673-6128-6Google Scholar
  15. Voegtlin, T.: Context quantization and contextual self-organizing maps. In: Proc. Int. Joint Conf. on Neural Networks, vol. 5, pp. 20–25 (2000)Google Scholar
  16. Lorenz, E.N.: The statistical prediction of solutions of dynamics equations. In: Proceedings International Symposium on Numerical Weather Prediction, pp. 629–635. Meteorological Society Japan (1962)Google Scholar
  17. Dunis, C.L., Williams, M.: Modelling and trading the EUR/USD exchange rate: Do neural network models perform better? Derivatives Use, Trading and Regulation 8(3), 211–239 (2002)Google Scholar
  18. Hellstrom, T., Holmstrom, K.: Predicting the Stock Market. Technical Report IMa-TOM-1997-07, Center of Mathematical Modeling, Department of Mathematics and Physis, Mälardalen University, Västeras, Sweden (August 1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Abir Jaafar Hussain
    • 1
  • Haya Al-Askar
    • 1
  • Dhiya Al-Jumeily
    • 1
  1. 1.Applied Computing Research GroupLiverpool John Moores UniversityLiverpoolUK

Personalised recommendations