Multistage RBF neural network ensemble learning for exchange rates forecasting

@article{Yu2008MultistageRN,
  title={Multistage RBF neural network ensemble learning for exchange rates forecasting},
  author={Lean Yu and Kin Keung Lai and Shouyang Wang},
  journal={Neurocomputing},
  year={2008},
  volume={71},
  pages={3295-3302}
}
In this study, a multistage nonlinear radial basis function (RBF) neural network ensemble forecasting model is proposed for foreign exchanger rates prediction. In the process of ensemble modeling, the first stage produces a great number of single RBF neural network models. In the second stage, a conditional generalized variance (CGV) minimization method is used to choose the appropriate ensemble members. In the final stage, another RBF network is used for neural network ensemble for prediction… CONTINUE READING
BETA

Citations

Publications citing this paper.
SHOWING 1-10 OF 55 CITATIONS, ESTIMATED 21% COVERAGE

55 Citations

0510'10'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 55 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 23 REFERENCES

Bias

  • L. Breiman
  • variance, and arcing classifiers, Technical…
  • 1994
Highly Influential
7 Excerpts

Similar Papers

Loading similar papers…