Multistage neural network metalearning with application to foreign exchange rates forecasting

Authors:
Kin Keung Lai;Lean Yu;Wei Huang;Shouyang Wang
Affiliations:
College of Business Administration, Hunan University, Changsha, China;Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong;School of Management, Huazhong University of Science and Technology, Wuhan, China;College of Business Administration, Hunan University, Changsha, China
Venue:
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Year:
2006

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Abstract

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.