Huang, Jih-JengJih-JengHuangTzeng, Gwo-HshiungGwo-HshiungTzengOng, Chorng-ShyongChorng-ShyongOng2008-10-222018-06-292008-10-222018-06-29200600963003http://ntur.lib.ntu.edu.tw//handle/246246/84963https://www.scopus.com/inward/record.uri?eid=2-s2.0-33645867652&doi=10.1016%2fj.amc.2005.08.032&partnerID=40&md5=d2be916dd097c3b934d39587f6fabbb2In this paper, a dynamic factor model is proposed to extract the dynamic factors from time series data. In order to deal with the problem of scaling, the cross-correlation matrices (CCM) are first employed to cluster the time series data. Then, the dynamic factors are extracted using the revised independent component analysis (ICA). In addition, a numerical study is used to demonstrate the proposed method. On the basis of the simulated results, we can conclude that the proposed method can really extract the effective dynamic factors. © 2005 Elsevier Inc. All rights reserved.application/pdf310814 bytesapplication/pdfen-USCross-correlation matrices (CCM); Dynamic factor model; Factor analysis; Independent component analysis (ICA); Time seriesAlgorithms; Computer simulation; Independent component analysis; Numerical methods; Problem solving; Time series analysis; Cross-correlation matrices (CCM); Dynamic factor models; Factor analysis; Time series data; Dynamic programmingjournal article2-s2.0-33645867652http://ntur.lib.ntu.edu.tw/bitstream/246246/84963/1/6.pdf