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 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.