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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Multistage Neural Network Metalearning with Application to Foreign Exchange Rates Forecasting
verfasst von : Kin Keung Lai, Lean Yu, Wei Huang, Shouyang Wang
Erschienen in: MICAI 2006: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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In this study, we propose a multistage neural network metalearning technique for financial time series predication. First of all, an interval sampling technique is used to generate different training subsets. Based on the different training subsets, the different neural network models with different training subsets are then trained to formulate different base models. Subsequently, to improve the efficiency of metalearning, the principal component analysis (PCA) technique is used as a pruning tool to generate an optimal set of base models. Finally, a neural-network-based metamodel can be produced by learning from the selected base models. For illustration, the proposed metalearning technique is applied to foreign exchange rate predication.