多元混沌时间序列的多核极端学习机建模预测

多元混沌时间序列广泛存在于自然、经济、社会、工业等领域. 对多元混沌时间序列进行建模预测有助于人类更好地管理, 控制与决策. 针对多元混沌时间序列的建模预测问题, 本文提出一种基于多核极端学习机的预测方法. 首先对多元混沌时间序列进行相空间重构, 将多元混沌时间序列序列的时间相关性转化为空间相关性. 提出一种结合多核学习算法与核极端学习机模型的多核极端学习机建立相空间中输入输出数据的非线性映射. 多核极端学习机模型结合了多核学习算法的数据融合能力以及核极端学习机的训练简便优势. 基于Lorenz混沌时间序列预测和San Francisco河流月径流量预测的仿真实验表明, 与其他常见混沌时间序列预测方法相比, 本文提出的基于多核极端学习机的多元混沌时间序列预测方法具有更小的预测误差.

Multivariate chaotic time series prediction using multiple kernel extreme learning machine

Multivariate chaotic time series is widely present in nature, such as in economy, society, industry and other fields. Modeling and predicting multivariate time series will help human to better manage, control, and make decision. A prediction method based on multiple kernel extreme learning machine is proposed in this paper to model the complex dynamics of multivariate chaotic time series. First, the multivariate chaotic time series is reconstructed in phase space, transforming the temporal correlation into spatial correlation. Then, a prediction model-multiple kernel extreme learning machine, which combines the multiple kernel learning and extreme learning machine with kernels, is proposed to approximate the nonlinear function of the input - output data in phase space. The proposed multiple kernel extreme learning machine could effectively combine the simple training of extreme learning machine with kernels and the data fusion capabilities of multiple kernel learning. Simulation results based on Lorenz chaotic time series prediction and San Francisco monthly runoff prediction demonstrate that, compared with other state-of-art chaotic time series prediction methods, the proposed multiple kernel extreme learning machine could get a better prediction accuracy.