Title :
Exchange Rates Forecasting with Least Squares Support Vector Machine
Author :
Liu, Lixia ; Wang, Wenjing
Author_Institution :
Sch. of Econ., Tianjin Univ. of Commerce, Tianjin
Abstract :
A novel forecasting model of foreign exchange market based on least squares support vector machine (LS-SVM) is proposed in this paper. The experiment on the prediction of four kinds of daily exchange rate recorded is carried out. Grid search method is used to determine the LS-SVM parameters automatically in the forecasting process. The results show the precision of fitting and forecasting are very high, which indicates that LS-SVM is a feasible and valid approach for forecasting exchange rate time series.
Keywords :
economic forecasting; exchange rates; learning (artificial intelligence); least squares approximations; search problems; support vector machines; time series; exchange rate time series; forecasting model; foreign exchange market; grid search method; least squares support vector machine; supervised learning; Artificial neural networks; Economic forecasting; Exchange rates; Lagrangian functions; Least squares methods; Prediction methods; Predictive models; Risk management; Support vector machine classification; Support vector machines; Exchange rate; Support vector machine; Time series; forecast;
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
DOI :
10.1109/CSSE.2008.140
