基于多重核学习支持向量回归的混沌时间序列预测

鉴于标准支持向量回归应用于混沌时间序列预测时经常会遇到诸如核函数及其参数难以确定的问题,提出了多重核支持向量回归的方法.通过在混合核空间求解二次约束下的二次规划问题,实现多重核支持向量回归算法.该算法不仅可以减少支持向量的个数,而且能够提高预测性能.最后将该方法运用到Lorenz, Henon和Mackey-Glass混沌时间序列预测,仿真结果表明该方法能够有效地提高预测精度,增强预测模型的泛化性能.关键词：混沌时间序列支持向量机多重核学习优化

Chaotic time series prediction based on multi-kernel learning support vector regression

Multi-kernel learning support vector regression (MKL-SVR) are proposed for chaotic time series prediction to solve the problems of kernel selection and hyper-parameter determination when using the standard SVR. The algorithm is realized through quadratic constrained quadratic programming (QCQP) in the hybrid kernel space, which not only reduces the number of support vectors, but also improves the prediction performance. Finally, it is applied to Mackey-Glass, Lorenz and Henon chaotic time series prediction. The results indicate that the proposed method can effectively increase the prediction precision, accelerate the convergency of cascade learning and enhance the generalization of prediction model.