Financial market forecasting using a two-step kernel learning method for the support vector regression
作者： Li Wang , Ji Zhu
DOI: 10.1007/S10479-008-0357-7
关键词: Kernel embedding of distributions 、 Artificial intelligence 、 Principal component regression 、 Support vector machine 、 Mathematical optimization 、 Radial basis function kernel 、 Kernel (statistics) 、 Least squares support vector machine 、 Polynomial kernel 、 Kernel method 、 Mathematics 、 Machine learning
摘要: In this paper, we propose a two-step kernel learning method based on the support vector regression (SVR) for financial time series forecasting. Given number of candidate kernels, our learns sparse linear combination these kernels so that resulting can be used to predict well future data. The L 1-norm regularization approach is achieve learning. Since parameter must carefully selected, facilitate tuning, develop an efficient solution path algorithm solves optimal solutions all possible values parameter. Our has been applied forecast S&P500 and NASDAQ market indices showed promising results.
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