Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Forecasting exchange rates using general regression neural networks
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A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
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ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Foreign Exchange Rates Forecasting with a C-Ascending Least Squares Support Vector Regression Model
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Exchange rate forecasting using flexible neural trees
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Stock index modeling using hierarchical radial basis function networks
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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This study proposes a novel forecasting approach – an adaptive smoothing neural network (ASNN) – to predict foreign exchange rates. In this new model, adaptive smoothing techniques are used to adjust the neural network learning parameters automatically by tracking signals under dynamic varying environments. The ASNN model can make the network training process and convergence speed faster, and make network’s generalization stronger than the traditional multi-layer feed-forward network (MLFN) model does. To verify the effectiveness of the proposed model, three major international currencies (British pounds, euros and Japanese yen) are chosen as the forecasting targets. Empirical analyses reveal that the proposed novel forecasting model outperforms the other comparable models. Furthermore, experimental results also show that the proposed model is an effective alternative approach for foreign exchange rate forecasting.