Novel SVM-based neural network ensemble model for foreign exchange rates forecasting

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Lean Yu
  • Shouyang Wang
  • Kin Keung Lai

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)717-723
Journal / PublicationJournal of Computational Information Systems
Volume1
Issue number4
Publication statusPublished - Dec 2005

Abstract

In this study, a triple-phase support vector machine based neural network ensemble model is proposed for exchange rates forecasting. In the first phase, many different single neural network models are generated. In the second phase, a conditional generalized variance minimization method is used to select the appropriate ensemble members. In the final phase, the support vector machine regression method is used for neural network ensemble for prediction purpose. For further illustration, two exchange rate series are used for testing. Empirical results obtained reveal that this novel neural network ensemble model can improve the performance of foreign exchange rates forecasting.

Research Area(s)

  • Ensemble, Foreign exchange rates forecasting, Neural network, Support vector machine

Citation Format(s)

Novel SVM-based neural network ensemble model for foreign exchange rates forecasting. / Yu, Lean; Wang, Shouyang; Lai, Kin Keung.

In: Journal of Computational Information Systems, Vol. 1, No. 4, 12.2005, p. 717-723.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review