Pso based neural network for time series forecasting

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2009, Pages 1422-1427

  Jha, Girish K ^a   Thulasiraman, Parimala ^a   Thulasiram, Ruppa K ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BACKPROPAGATION TRAINING ALGORITHM; FINANCIAL TIME SERIES FORECASTING; PARTICLE SWARM ALGORITHM; PARTICLE SWARM OPTIMIZATION ALGORITHM; PERFORMANCE MEASURE; TIME SERIES FORECASTING; TRAINED NEURAL NETWORKS;

ALGORITHMS; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; FINANCIAL DATA PROCESSING; FORECASTING; PARTICLE SWARM OPTIMIZATION (PSO); TIME SERIES;

NEURAL NETWORKS;

EID: 70449384987     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2009.5178707     Document Type: Conference Paper

References (29)

1. G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden Day, 1976.
2. C. Granger and A. Anderson, An introduction to Bilinear Time Series Models. Gottingen: Vandenhoeck and Ruprecht, 1978.
3. H. Tong, Non-linear Time Series. Oxford: Clarendon Press, 1990.
4. R. Engle, "Autoregressive conditional heteroskedasticity, with estimates of the variance of UK inflation, " Journal of Econometrica, vol. 50, 1982.
5. T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity, " Journal of Econometrics, vol. 31, 1986.
6. C. Chatfield, The Analysis of Time Series: An Introduction. Washington D. C.: Chapman Hall/CRC, 1996.
7. G. Zhang, B. Patuwo, and M. Hue, "Forecasting with artificial neural networks: The state of the art, " International Journal of Forecasting, vol. 14, 1998.
8. D. Rumelhart, J. McClelland, and the PDP Group, Parallel Distributed Processing, Explorations in the Microstructure of Cognition. Cambridge: MIT Press, 1986, Vol. I.
9. G. Zhang, "Avoiding pitfalls in neural network research, " IEEE Transaction on Systems, Man, and Cybernetics-Part C: Applications & Reviews, vol. 37, 2007.
10. D. Fogel, Evolutionary Computation: The Fossil Record. Piscataway NJ: IEEE Press, 1998.
11. J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence. New York: Morgan Kaufmann, 2001.
12. A. Brabazon and M. O'Neil, Biologically Inspired Algorithms for Financial modelling. New York: Springer, 2006.
13. M. Clerc, Particle Swarm Optimization. London: ISTE, 2006.
14. J. Kennedy and R. Eberhart, "Particle Swarm Optimization, " in Proc. IEEE Internation Conference on Neural Networks, Perth, Australia, 1995.
15. Y. Chen and A. Abraham, "Hybrid Neurocomputing for Breast Cancer Detection, " in Fourth International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST05), 2005, pp. 884- 892.
16. Y. Chen, B. Yanga, and J. Dong, "Time-series prediction using a local linear wavelet neural network, " Neurocomputing, vol. 69, pp. 449-465, 2006.
17. H. Demuth and M. Beale, Neural Network Toolbox User's Guide. Natic, MA: Mathworks, 2002.
18. H. White, "Learning in artificial neural networks: A statistical perspective, " Neuralcomputation, vol. 1, 1989.
19. G. Cybenko, "Approximation by superpositions of a sigmoid function, " Mathematics of Control Signals and Systems, vol. 2, 1989.
20. G. Zhang, "Time series forecasting using a hybrid ARIMA and neural network model, " Neurocomputing, vol. 50, 2003.
21. D. Patterson, Artificial Neural Networks: Theory and Applications. Singapore: Prentice Hall, 1996.
22. F. Azevedo, Z. A. Vale, and P. de Moura Oliviera, "A Decision- Support System Based on Particle Swarm Optimization for Multiperiods Hedging in Electricity Markets, " IEEE transactions on power systems, vol. 22, no. 3, August 2007.
23. A. Abrahams and M. R. Khan, "Neuro-Fuzzy Paradigms for Intelligent Energy Management Innovations in Intelligent Systems: Design, Management and Applications, " Studies in Fuzziness and Soft Computing, pp. 285-314, 2003.
24. I. Trelea, "The particle swarm optimization algorithm: convergence analysis and parameter selection, " Information Processing Letters, vol. 85, 2003.
25. B. Birge, "PSOt-A Particle Swarm Optimization Toolbox for use with Matlab, " in Proceedings of the 2003 IEEE Swarm Intelligence Symposium, 2003, pp. 182-186.
26. N. Kohzadi, M. Boyd, B. Kermanshahi, and I. Kaastra, "A comparison of artificial neural network and time series models for forecasting commodity prices, " Neurocomputing, vol. 10, 1996.
27. M. Qi and G. Zhang, "An investigation of model selection criteria for neural network time series forecasting, " European Journal of Operation Research, vol. 132, 2001.
28. Y. Shi and R. Eberhart, "A Modified Particle Swarm Optimizer, " in International Conference on Evolutionaty Computation, Anchorage, Alaska, may 1998, pp. 69-73.
29. R. Meese and K. Rogoff, "Empirical exchange rate models of the seventies: do they fit out-of-sample, " Journal of International Economics, vol. 14, 1983.