Abstract
In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model’s performance with some existing network ensemble approaches in terms of three exchange rates series. Experimental results reveal that the prediction using the proposed approach is consistently better than those obtained using the other methods presented in this study in terms of the same measurements.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yu, L., Huang, W., Lai, K.K., Wang, S. (2006). A Reliability-Based RBF Network Ensemble Model for Foreign Exchange Rates Predication. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_43
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DOI: https://doi.org/10.1007/11893295_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
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