摘要
针对常见的降维方法难以有效保留多元时间序列主要特征的问题,分析了传统主成分分析(PCA)方法在多元时间序列降维中的局限性,提出一种基于共同主成分分析的多元时间序列降维方法,并通过仿真实验比较了两种方法的降维有效性和计算复杂度.实验结果表明,所提出的降维方法能够以相对较小的计算代价,更有效地对多元时间序列进行降维.
Existing dimension reduction method for multivariate time series can＇t preserve their feature effectively. Therefore, the drawback of PCA method is analyzed, when it is used in MTS dimension reduction, and based on common principal component analysis, a dimension reduction method for multivariate time series is proposed. The computational complexity and the validity of dimension reduction are compared between different methods. The results of experiments show that the proposed method can reduce dimension effectively at comparatively low computational cost, and at the same time preserve most feature of multivariate time series.
出处
《控制与决策》
EI
CSCD
北大核心
2013年第4期531-536,共6页
Control and Decision
关键词
降维
多元时间序列
主成分分析
共同主成分分析
计算复杂度
dimension reduction~ multivariate time series~ principal component analysis
common principal component analysis
computational complexity
