Time series forecasting methodology for multiple-step-ahead prediction

Proceedings of the IASTED International Conference on Computational Intelligence

Volumn 2005, Issue , 2005, Pages 456-461

  Pavlidis, N G ^a   Tasoulis, D K ^a   Vrahatis, M N ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

Clustering; Computational intelligence; Forecasting; Neu ral networks

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; NEURAL NETWORKS; TIME SERIES ANALYSIS;

CLUSTERING; EXCHANGE RATE; TIME SERIES FORECASTING;

TECHNOLOGICAL FORECASTING;

EID: 33748563066     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. R. Bakker, J. C. Scheuten, C. L. Giles, F. Hakens, and C. M. van den Bleek, Learning of chaotic attractors by neural networks, Neural Computation 12 (2000), no. 10, 2355-2383.
2. C. Diks, W. R. van Zwet, F. Hakens, and J. DeGoede, Detecting differences between delay vector distributions, Physical Review E 53 (1996), no. 3, 2169-2176.
3. J. D. Farmer and J. J. Sidorowich, Predicting chaotic time series, Physical Review Letters 59 (1987), no. 8, 845-848.
4. L. C. Giles, S. Lawrence, and A. H. Tsoi, Noisy time series prediction using a recurrent neural net-work and grammatical inference, Machine Learning 44 (2001), no. 1/2, 161-183.
5. K. Hornik, Multilayer feedforward networks are universal approximators, Neural Networks 2 (1989), 359-366.
6. C. Igel and M. Hüsken, Improving the Rprop learning algorithm, Proceedings of the Second International ICSC Symposium on Neural Computation (NC 2000) (H. Bothe and R. Rojas, eds.), ICSC Academic Press, 2000, pp. 115-121.
7. M. B. Kennel, R. Brown, and H. D. Abarbanel, Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A 45 (1992), no. 6, 3403-3411.
8. G.D. Magoulas, V.P. Plagianakos, and M.N. Vrahatis, Adaptive stepsize algorithms for on-line training of neural networks, Nonlinear Analysis, T.M.A. 47 (2001), no. 5, 3425-3430.
9. G.D. Magoulas, M.N. Vrahatis, and G.S. Androulakis, Effective backpropagation training with variable stepsize, Neural Networks 10 (1997), no. 1, 69-82.
10. T. P. Meyer and N. H. Packard, Local forecasting of high dimensional chaotic dynamics, Nonlinear Modelling and Forecasting (M. Casdagli and S. Eubank, eds.), Addison-Wesley, 1992, pp. 249-264.
11. M. Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks 6 (1993), 525-533.
12. N. G. Pavlidis, D. K. Tasoulis, and M. N. Vrahatis, Financial forecasting through unsupervised clustering and evolutionary trained neural networks, Proceedings of the Congress on Evolutionary Computation (CEC 2003), 2003, pp. 2314-2321.
13. A. Pinkus, Approximation theory of the MLP model in neural networks, Acta Numerica (1999), 143-195.
14. J. C. Principe, A. Rathie, and J. M. Kuo, Prediction of chaotic time series with neural networks and the issue of dynamic modeling, International Journal of Bifurcation and Chaos 2 (1992), no. 4, 989-996.
15. J. C. Principe, L. Wang, and M. A. Motter, Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control, Proceedings of the IEEE, no. 6, 1998, pp. 2240-2257.
16. M. Riedmiller and H. Braun, A direct adaptive method for faster backpropagation learning: The rprop algorithm, Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA, 1993, pp. 586-591.
17. M.N. Vrahatis, B. Boutsinas, P. Alevizos, and G. Pavlides, The new k-windows algorithm for improving the k-means clustering algorithm, Journal of Complexity 18 (2002), 375-391.
18. S. Walczak, An empirical analysis of data requirements for financial forecasting with neural networks, Journal of Management Information Systems 17 (2001), no. 4, 203-222.
19. A. S. Weigend, M. Mangeas, and A. N. Srivastava, Nonlinear gated experts for time series: Discovering regimes and avoiding overfitting, International Journal of Neural Systems 6 (1995), 373-399.
20. H. White, Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings, Neural Networks 3 (1990), 535-549.