Ezchange Rate Movement Direction Forecasting Ezpert: Polynomial Smooth Support Vector Machine

摘要：

Using the machine learning methods to predict the financial time series movement direction is a very interesting topic. Among of these machine learning methods, support vector machine (SVM) is the most effective and intelligent one based on statistical learning theory. A new support vector machine is presented in this paper, called it polynomial smooth support vector machine (PSSVM). After being solved by BFGS method, the predict parameters are obtained. Application of forecasting exchange rate movement direction of RMB(Chinese renminbi) vs USD(United States Dollars) with Dow Jones China Index Series is investigated. Many results hold the effective of this new prediction method.

关键词： Daca Mining Support vector machines Financial time series forecasting

作者: Yubo Yuan Feilong Cao

作者单位: Institute of Metrology and Computational Science, China Jiliang University, Hangzhou 310018, P.R. China

会议类型: 国际会议

会议名称: The First World Congress on Global Optimization in Engineering & Science(第一届工程与科学全局优化国际会议 WCGO2009)

会议地点: 长沙

会议语种:英文

页码: 618-623

在线出版日期: 2009-06-01（万方平台首次上网日期，不代表论文的发表时间）