This paper will investigate the application of multiobjective evolutionary 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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Chiam, S. C., Tan, K. C., & Al Mamun, A. (2007). Multiobjective evolutionary neural networks for time series forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4403 LNCS, pp. 346–360). Springer Verlag. https://doi.org/10.1007/978-3-540-70928-2_28
Mendeley helps you to discover research relevant for your work.