Chaos-based support vector regressions for exchange rate forecasting

This study implements a chaos-based model to predict the foreign exchange rates. In the first stage, the delay coordinate embedding is used to reconstruct the unobserved phase space (or state space) of the exchange rate dynamics. The phase space exhibits the inherent essential characteristic of the...

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Bibliographic Details
Published in:Expert systems with applications 2010-12, Vol.37 (12), p.8590-8598
Main Authors: Huang, Shian-Chang, Chuang, Pei-Ju, Wu, Cheng-Feng, Lai, Hiuen-Jiun
Format: Article
Language:English
Subjects:
Chaos theory
Exchange rate forecasting
Hybrid model
Kernel method
Support vector machine
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Summary:This study implements a chaos-based model to predict the foreign exchange rates. In the first stage, the delay coordinate embedding is used to reconstruct the unobserved phase space (or state space) of the exchange rate dynamics. The phase space exhibits the inherent essential characteristic of the exchange rate and is suitable for financial modeling and forecasting. In the second stage, kernel predictors such as support vector machines (SVMs) are constructed for forecasting. Compared with traditional neural networks, pure SVMs or chaos-based neural network models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2010.06.001