物理学报Issue(12)：1-7,7.DOI:10.7498/aps.62.120510
一种基于误差补偿的多元混沌时间序列混合预测模型*
A hybrid prediction model of multivariate chaotic time series based on error correction∗
摘要
Abstract
Considering the problem that simply modifying the reservoir algorithm cannot significantly improve the prediction accuracy of chaotic multivariate time series, in this paper we propose a hybrid prediction model based on error correction. The observed data includes both linear and nonlinear features. First, we use autoregressive and moving average model to capture the linear features, then build a regularized echo state network to portray the dynamic nonlinear features. Finally, we add the predicted nonlinear value to the predicted linear value, in order to improve forecasting accuracy achieved by either of the models used separately. The experimental results of Lorenz and Sunspot-Runoff in the Yellow River time series demonstrate the effectiveness and characteristics of the proposed model herein.关键词
回声状态网络/混沌/多元时间序列预测/误差补偿Key words
echo state network/chaos/multivariate time series prediction/error correction引用本文复制引用
韩敏,许美玲..一种基于误差补偿的多元混沌时间序列混合预测模型*[J].物理学报,2013,(12):1-7,7.基金项目
国家自然科学基金(批准号:61074096)资助的课题 （批准号:61074096）
