Canonical Correlation Analysis of Time-Series and Stochastic Realization

• Katayama Tohru

  Department of Applied Mathematics and Physics, Graduate School of Informatics Kyoto University

抄録

In this paper, first we give a brief introduction to the canonical correlation analysis (CCA) for two sets of random variables, and present a method of computing the canonical correlations by using the singular value decomposition (SVD). After introducing an innovation model, we state the stochastic realization problem, together with some definitions. We show that predictor spaces play the role of memory for exchange information between the past and future in stochastic dynamical systems. Then we review the classical balanced stochastic realization results based on the CCA between the past and future of a stationary time series. Moreover, defining the conditional canonical correlations between the past and future of a stochastic system in the presence of exogenous inputs, we derive a stochastic realization algorithm with exogenous inputs. A numerical result is also included.

収録刊行物

• Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications

  Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2004 (0), 1-10, 2004-05-05

  システム制御情報学会ストカスティックシステムシンポジウム

キーワード

• Materials Science (miscellaneous)

詳細情報

• CRID: 1390564237988242944
• NII論文ID: 130007377012
• DOI: 10.5687/sss.2004.1
• ISSN: 21884749
• ISSN: 21884730
• Web Site: https://www.jstage.jst.go.jp/article/sss/2004/0/2004_1/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可