A Novel Nonlinear Neural Network Ensemble Model for Financial Time Series Forecasting
In this study, a new nonlinear neural network ensemble model is proposed for financial time series forecasting. In this model, many different neural network models are first generated. Then the principal component analysis technique is used to select the appropriate ensemble members. Finally, the support vector machine regression method is used for neural network ensemble. For further illustration, two real financial time series are used for testing.
KeywordsRoot Mean Square Error Neural Network Model Ensemble Member Ensemble Method Financial Time Series
- 3.Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
- 5.Perrone, M.P., Cooper, L.N.: When Networks Disagree: Ensemble Methods for Hybrid Neural Networks. In: Mammone, R.J. (ed.) Neural Networks for Speech and Image Processing, pp. 126–142. Chapman-Hall, Boca Raton (1993)Google Scholar
- 6.Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. In: Tesauro, G., Touretzky, D., Leen, D. (eds.) Advances in Neural Information Processing Systems, pp. 231–238. The MIT Press, Cambridge (1995)Google Scholar
- 7.Breiman, L.: Combining Predictors. In: Sharkey, A.J.C. (ed.) Combining Artificial Neural Nets – Ensemble and Modular Multi-net Systems, pp. 31–50. Springer, Berlin (1999)Google Scholar