Author:
Alshammri Fayed,Pan Jiazhu
Abstract
AbstractThis paper proposes an extension of principal component analysis to non-stationary multivariate time series data. A criterion for determining the number of final retained components is proposed. An advance correlation matrix is developed to evaluate dynamic relationships among the chosen components. The theoretical properties of the proposed method are given. Many simulation experiments show our approach performs well on both stationary and non-stationary data. Real data examples are also presented as illustrations. We develop four packages using the statistical software R that contain the needed functions to obtain and assess the results of the proposed method.
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability
Reference29 articles.
1. Alshammri F (2020a) MACF: moving auto- and cross-correlation function. R package version
2. Alshammri F (2020b) MCOV: moving cross-covariance matrix. R package version
3. Alshammri F (2020c) MDPCA: moving dynamic principal component analysis for non-stationary multivariate time series. R package version
4. Alshammri F (2020d) RCCM: retained component criterion for the moving dynamic principal component analysis. R package version
5. Bai J, Ng S (2002) Determining the number of factors in approximate factor models. Econometrica 70(1):191–221
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献