A Factor Analysis for Time Series.

Abstract

This paper studies how to identify hidden factors in multivariate time series process. It is shown that the number of factors must be equal to the rank of both the covariance matrices and the parameter matrices of the infinite moving average representation of the process. A canonical transformation is derived which can recover such factors. The method is illustrated with several examples. Originator-supplied keywords include: Multivariate ARMA process, Canonical analysis, Eigenvalues and Eigenvectors.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1984
Accession Number
ADA147495

Entities

People

  • D. Pena
  • George E. P. Box

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Algebra
  • Covariance
  • Data Science
  • Eigenvalues
  • Eigenvectors
  • Equations
  • Factor Analysis
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • Noise
  • Sequences
  • Simulations
  • Standards
  • Statistics
  • United States
  • White Noise

Fields of Study

  • Engineering
  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Linear Algebra