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.
出处
《计算机应用》
CSCD
北大核心
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)
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
energy consumption
multiple time series
vector-valued function learning
multi-task learning
autoregressive method
Support Vector Regression （SVR）
