The nature of statistical learning theory
The nature of statistical learning theory
Neural network models for time series forecasts
Management Science
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
A hybrid model for exchange rate prediction
Decision Support Systems
Neural Network Model Coupled with Phase Space Reconstruction and Its Application
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Expert Systems with Applications: An International Journal
Online option price forecasting by using unscented Kalman filters and support vector machines
Expert Systems with Applications: An International Journal
Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multi-agent modeling of multiple FX-markets by neural networks
IEEE Transactions on Neural Networks
Using Gaussian process based kernel classifiers for credit rating forecasting
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Hi-index | 12.05 |
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.