基于支持向量回归的多时间序列自回归方法  

Multiple time series autoregressive method based on support vector regression

作　　者：张伟[1] 柳先辉[1] 丁毅[2] 史德明[2] 

机构地区：[1]同济大学电子与信息工程学院,上海201804 [2]马鞍山钢铁股份有限公司能源与环境保护部,安徽马鞍山243000

出　　处：《计算机应用》2012年第9期2508-2511,2519,共5页journal of Computer Applications

基　　金：国家863计划项目(2009AA043503);国家科技支撑计划项目(2012BAF10B05)

摘　　要：能耗时间序列涉及多种能源,且各种能源间关系复杂,主要通过多个独立的单时间序列进行预报,这种方式忽略了多时间序列之间的依赖性。为了充分利用多时间序列之间的关联信息以提高预报的准确性,根据机器学习中的向量值函数学习和多任务学习理论,采用支持向量回归(SVR)算法建立了多时间序列的向量值自回归方法和多任务自回归方法。实验结果证明,与多个独立的单时间序列模型相比,通过这种方法建立的多时间序列自回归模型在焦化工序能耗预报中表现出了更好的性能。Energy consumption time series involves a variety of energy and the relationship between different energy is complicated. Most existing consumption methods make prediction through multiple independent single time series respectively, which ignores dependencies between multiple time series. In order to take full advantage of the association between multiple time series and improve prediction accuracy, the vector-valued autoregressive method and multi-task autoregressive method based on Support Vector Regression （SVR） machines were proposed for multiple time series forecast according to vector-valued function learning and multi-task learning theory. The experimental results with energy consumption of coking process verify that multiple time series autoregressive models based on the proposed methods show better prediction performance.

关 键 词：能耗 多时间序列 向量值函数学习 多任务学习 自回归方法 支持向量回归 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]