On utilizing self-organizing fuzzy neural networks for financial forecasts in the NN5 forecasting competition

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2010, Pages

  Coyle, Damien ^a   Prasad, Girijesh ^a   McGinnity, T Martin ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY INFERENCE; FUZZY NEURAL NETWORKS; LEARNING ALGORITHMS; TIME SERIES;

CASH MACHINES; DATA DYNAMICS; DAY-AHEAD; FINANCIAL FORECASTS; LEARN+; LEARNING PROCESS; PREDICTION ACCURACY; SELF-ORGANIZING FUZZY NEURAL NETWORK; TIME SERIES PREDICTION; TIMES SERIES;

FORECASTING;

EID: 79959462360     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2010.5596955     Document Type: Conference Paper

References (35)

1. P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, Springer-Verlag, New York, 1996.
2. J.-S.R. Jang, "Neuro-Fuzzy & Soft Computing", Prentice Hall, 1997.
3. Y. Tan and A. V. Cauwenberghe, "Neural-network based d-step-ahead predictors for non-linear systems with time delay", Engineering Applications of Artificial Intelligence, vol. 12, pp. 21-35, 1999.
4. J. Ou and R. R. Rhinehart, "Grouped neural network model predictive control", Control Engineering Practice, vol. 11, pp. 723-732, 2003.
5. J. A. Rossiter and B. Kouvaritakis, "Modelling and implicit modelling for predictive control", International Journal on Control, vol. 74, no. 11, pp. 1085-1095, 2001.
6. D. Coyle, G. Prasad and T.M. McGinnity, "A time-series prediction approach for feature extraction in a brain-computer interface", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 4, pp. 461-467, Dec. 2005.
7. D. Coyle, T.M. McGinnity, and G. Prasad, "Enhancing autonomy and computational efficiency of the self-organizing fuzzy neural network for a brain-computer interface", FUZZ-IEEE, World Congress on Computational Intelligence, pp. 10485-10492, July, 2006.
8. D. Coyle, Intelligent Preprocessing and Feature Extraction Techniques for a Brain Computer Interface, PhD Thesis, Faculty of Computing and Engineering, University of Ulster, N. Ireland, 2006.
9. G. Leng, G. Prasad, T.M. McGinnity, "An on-line algorithm for creating self-organising fuzzy neural networks", Neural Networks, vol. 17, issue 10, pp 1477-1493, 2004.
10. G. Leng, Algorithmic Developments for Self-Organizing Fuzzy Neural Networks, PhD Thesis, University of Ulster, Sept. 2003.
11. S. Wu, & M.J. Er, "Dynamic fuzzy neural networks-a novel approach to function approximation", IEEE Trans. on Syst, Man, and Cybern, Part B: Cybernetics, vol. 30, no. 2, pp. 358-364, April, 2000.
12. N. K. Kasabov, "Evolving connectionist systems: methods and applications in bioinformatics", Springer, 2003.
13. nd Ed.), Springer, 2007.
14. P.P. Angelov and D. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models", IEEE Trans. on Systems, Man and CybernetiCS, part B, Vol. 34 No. 1, pp. 484-498, 2004.
15. E. Lughofer and E.P. Klement, "FLEXFIS: A Variant for Incremental Learning of Takagi-Sugeno Fuzzy Systems", Proceedings of FUZZIEEE 2005, pp. 915-920, 2005
16. N. K. Kasabov, "Evolving Fuzzy Neural Networks for Supervised/Unsupervised Online Knowledge-Based Learning", IEEE Transactions on Systems, Man, and Cybernetics, vol. 31, no. 6, pp. 902-918, December 2001.
17. N. K. Kasabov, and Q. Song, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction", IEEE Transactions on Fuzzy Systems,. vol. 10, no. 2, pp. 144-154, April 2002.
18. C. S. Leung, K. W. Wong, P. F. Sum, and L. W. Chan, "A pruning method for recursive least squares algorithm", Neural Networks, vol. 14, pp. 147-174, 2001.
19. B. Hassibi and D. G. Stork, "Second order derivatives for network pruning: Optimal brain surgeon", Advances in Neural Information Processing Systems 4, pp. 164-171, 1993.
20. D. Coyle, G. Prasad, and T.M. McGinnity, "Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface", IEEE Transactions on Systems, Man and Cybernetics (Part B), Vol. 39, Issue 6, pp. 1458-1471, Dec. 2009.
21. S. F. Crone, "WCCI 2008 Results of the NN5 Time Series Forecasting Competition, Predictions of cash withdrawals at ATM-machines, World Congress on Computational Intelligence, 2008. (to be published)
22. The NN5 Artificial Neural Network and Computational Intelligence Forecasting Competition, www.neural-forecasting-competition.com
23. A. Schlogl, D. Flotzinger, G. Pfurtscheller. "Adaptive autoregressive modeling used for single-trial EEG classification", Biomedizinische Technik, vol. 42, pp. 162-167, 1997.
24. G.P. Williams, Chaos Theory Tamed, Taylor and Francis, 1997.
25. M. Palus, L. Pecen, D. Pivka, "Estimating predictability: The redundancy and surrogate data method", Neural Network World., Vol. 5 (4) pp. 537-552, 1995.
26. nd International BCI Workshop, Biomed .. Technik, vol. 49, no. 1. pp. 43-44, Sept. 2004.
27. T. Takagi and M. Sugeno, "Derivation of fuzzy control rules from human operator"s control action", Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, pp. 55-60, 1983.
28. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modelling and control", IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116-132, 1985.
29. B. Hassibi and D. G. Stork, "Second order derivatives for network pruning: Optimal brain surgeon", Advances in Neural Information Processing Systems 4, pp. 164-171, 1993.
30. C. C. Lee, "Fuzzy logic in control systems: Fuzzy logic controller - Part I and II", IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no. 2, pp. 287-294, 1985.
31. M. T. Hagan and M. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989-993, 1994.
32. nd ed). John Wiley & Sons, 2001.
33. A. Rizzi, M. Panella and F. M. F. Mascioli, "Adaptive resolution minmax classifiers", IEEE Transactions on Neural Networks, vol. 13, no. 2, March 2002.
34. K. Rashid, J. A. Ramirez and E M. Freeman, "A general approach for extracting sensitivity analysis from a neuro-fuzzy model", IEEE Transactions on Magnetics, vol. 36, no. 4, July 2000.
35. D. Coyle, G. Prasad and T.M. McGinnity, "Faster Self-organising Fuzzy Neural Network Training and Improved Autonomy with Time-Delayed Synapses for Locally Recurrent Learning", in System and Circuit Design for Biologically-Inspired Learning, IGI-global, pp. xx-xx, 2010. (invited chapter - in press)