Application of Independent Component Analysis Preprocessing and Support Vector Regression in Time Series Prediction

摘要：

In this study, the application of independent component analysis (ICA), a new feature extraction method, and support vector regression (SVR) in time series prediction is presented. The proposed method first use ICA as preprocessing to transform the input space composed of original time series data into the feature space consisting of independent components (ICs) representing underlying information/features of the original data. Then, prediction models will be built by using SVR for ICs. Finally, the predicted value of each IC will be transformed back into the original space for time series prediction. Experimental results on the forecasting of NTD/USD exchange rate have showed that the proposed method outperforms the SVR model without ICA preprocessing.

作者: Chi-Jie Lu Jui-Yu Wu Tian-Shyug Lee

作者单位: Dept.of Industrial Engineering and Management, Ching Yun University Dept.of Business Administration, Lunghwa University of Science and Technology Graduate Institute of Management, Fu Jen Catholic University

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 468-471

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）