CAJ | 학술논문

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.
침대상견적강유방법난이유효보류다원시간서렬주요특정적문제,분석료전통주성분분석(PCA)방법재다원시간서렬강유중적국한성,제출일충기우공동주성분분석적다원시간서렬강유방법,병통과방진실험비교료량충방법적강유유효성화계산복잡도.실험결과표명,소제출적강유방법능구이상대교소적계산대개,경유효지대다원시간서렬진행강유.
@@@@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.