Chaos-based support vector regressions for exchange rate forecasting

Shian Chang Huang, Pei Ju Chuang, Cheng Feng Wu, Hiuen Jiun Lai

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


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 exchangerate 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 rootmean- squared forecasting errors are significantly reduced.

Original languageEnglish
Pages (from-to)8590-8598
Number of pages9
JournalExpert Systems with Applications
Issue number12
Publication statusPublished - 2010 Dec

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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