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Multiobjective Evolutionary Neural Networks for Time Series Forecasting

  • Swee Chiang Chiam
  • Kay Chen Tan
  • Abdullah Al Mamun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4403)

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

Time Series Forecasting Multiobjective Evolutionary Neural Network 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Swee Chiang Chiam
    • 1
  • Kay Chen Tan
    • 1
  • Abdullah Al Mamun
    • 1
  1. 1.Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576Singapore

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