Exchange Rates Forecasting with Least Squares Support Vector Machine

Author

Liu, Lixia ; Wang, Wenjing

Author_Institution

Sch. of Econ., Tianjin Univ. of Commerce, Tianjin

Volume

5

fYear

2008

fDate

12-14 Dec. 2008

Firstpage

1017

Lastpage

1019

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Software Engineering, 2008 International Conference on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-0-7695-3336-0

Type

conf

DOI

10.1109/CSSE.2008.140

Filename

4723077