文献类型：期刊文献

英文题名：Forecasting the movement direction of exchange rate with polynomial smooth support vector machine

作者：Yuan, Yubo[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2013

卷号：57

期号：3-4

起止页码：932

外文期刊名：MATHEMATICAL AND COMPUTER MODELLING

收录：;EI(收录号：20125015781596);WOS:【SSCI(收录号:WOS:000311911700055)，SCI-EXPANDED(收录号:WOS:000311911700055)】；

基金：The research was supported by the National Natural Science Foundations of China (Nos, 61001200, 61101239) and the Natural Science Foundation of Zhejiang Province of China (No. Y6100010).

语种：英文

外文关键词：Data mining; Machine learning; Support vector machines; Neural networks; Financial time series; Forecasting

摘要：It is a very interesting topic to forecast the movement direction of financial time series by machine learning methods. Among these machine learning methods, support vector machine (SVM) is the most effective and intelligent one. A new learning model is presented in this paper, called the polynomial smooth support vector machine (PSSVM). After being solved by Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, optimal forecasting parameters are obtained. The exchange rate movement direction of RMB (Chinese renminbi) vs USD (United States Dollars) is investigated. Six indexes of Dow Jones China Index Series are used as the input. 4 sections with 180 time experiments have been completed. Many results show that the proposed learning model is effective and powerful. (C) 2012 Elsevier Ltd. All rights reserved.