Abstract
This paper will investigate the application of multiobjective evolu-tionary neural networks in time series forecasting. The proposed algorithmic model considers training and validation accuracy as the objectives to be optimized simultaneously, so as to balance the accuracy and generalization of the evolved neural networks. To improve the overall generalization ability for the set of solutions attained by the multiobjective evolutionary optimizer, a simple algorithm to filter possible outliers, which tend to deteriorate the overall performance, is proposed also. Performance comparison with other existing evolutionary neural networks in several time series problems demonstrates the practicality and viability of the proposed time series forecasting model.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1999)
Fieldsend, J., Singh, S.: Pareto Evolutionary Neural Networks. IEEE Transactions on Neural Networks 16(2), 338–354 (2005)
Geman, S., Bienenstock, E., Doursat, R.: Neural Networks and the Bias/Variance Dilemma. Neural Computation 4, 1–58 (1992)
Armstrong, J., Collopy, F.: Error measures for generalizing about forecasting methods-Empirical Comparisons. International Journal of Forecasting 8(1), 69–80 (1992)
Makridakis, S.G., Wheelwright, S.C.: Forecasting methods and applications. Wiley, New York (1998)
Ash, T.: Dynamic node creation in backpropagation networks. Connection Science 1(4), 365–375 (1989)
Lehtokangas, M.: Modeling with constructive backpropagation. Neural Networks 12, 707–716 (1999)
Miller, G., Todd, P., Hegde, S.: Designing neural networks using genetic algorithms. In: Third International Conference on Genetic Algorithms, pp. 379–384 (2000)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)
Abraham, A., Grosan, C., Han, S.Y., Gelbukh, A.: Evolutionary Multiobjective Optimization Approach for Evolving Assemble of Intelligent Paradigms for Stock Market Modeling. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 673–681. Springer, Heidelberg (2005)
Abbass, H.A.: Pareto Neuro-Evolution: Constructing Ensemble of Neural Networks Using Multi-objective Optimization. In: IEEE Congress on Evolutionary Computation, vol. 3, pp. 2074–2080. IEEE, Los Alamitos (2003)
Abbass, H.A.: A Memetic Pareto Evolutionary Approach to Artificial Neural Networks. In: Australian Joint Conference on Artificial Intelligence, pp. 1–12 (2001)
Abbass, H.A.: An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine 25(3), 265–281 (2002)
Teixeira, R.D.A., Braga, A.D.P., Takahashi, R.H.C., Saldanha, R.R.: A Multi-Objective Optimization Approach for Training Artificial Neural Networks. In: Brazilian Symposium on Neural Networks, pp. 168–172 (2000)
Oliveira, L.S., Morita, M., Sabourin, R.: Multi-Objective Genetic Algorithms to Create Ensemble of Classifiers. In: 3rd International Conference on Evolutionary Multi-Criterion Optimization, pp. 592–606 (2005)
Jin, Y., Okabe, T., Sendhoff, B.: Neural network regularization and ensembling using multi-objective evolutionary algorithms. In: Congress on Evolutionary Computation , pp. 1–8 (2004)
Chandra, A., Yao, X.: DIVACE: Diverse and Accurate Ensemble Learning Algorithm. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 619–625. Springer, Heidelberg (2004)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorization. Journal of Information Fusion 6(1), 5–20 (2005)
Prechelt, L.: Proben1-A Set of Neural Network Benchmarking Problems and Benchmarking Rules. Technical Report Nr. 21/ 94, Faculty of Computer Science, University of Karlsruhe, Germany (1994)
Radtke, P.V.W., Wong, T., Sabourin, R.: An evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization. In: IEEE Congress on Evolutionary Computation, pp. 6359–6366. IEEE Computer Society Press, Los Alamitos (2006)
Tong, H.: Threshold models in non-linear time series analysis. Lecture notes in Statistics, vol. 21. Springer, Heidelberg (1983)
Cortez, P., Rocha, M., Neves, J.: Evolving Time Series Forecasting Neural Network Models. In: International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Models (2001)
Cortez, P., Rocha, M., Neves, J.: A Meta-Genetic Algorithm for Time Series Forecasting. In: Workshop on Artificial Intelligence Techniques for Financial Time Series Analysis, 10th Portuguese Conference on Artificial Intelligence, pp. 21–31 (2001)
Hyndman, R.J.: Time Series Data Library. http://www.robhyndman.info/TSDL/ , Accessed on 14.10.2006
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Chiam, S.C., Tan, K.C., Mamun, A.A. (2007). Multiobjective Evolutionary Neural Networks for Time Series Forecasting. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-70928-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70927-5
Online ISBN: 978-3-540-70928-2
eBook Packages: Computer ScienceComputer Science (R0)