A Canonical Analysis of Multiple Time Series.

Abstract

This paper proposes a canonical transformation of a k dimensional stationary autoregressive process. The components of the transformed process are ordered from least predictable to most predictable. The least predictable components are often nearly white noise and the most predictable can be nearly nonstationary. Transformed variables which are white noise can reflect relationships which may be associated with or point to economic or physical laws. A 5-variate example is given.

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1975
Accession Number
ADA023861

Entities

People

  • G. C. Tiao
  • George E. P. Box

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Noise
  • White Noise

Readers

  • Statistical inference.
  • Theoretical Analysis.