Modeling of Multiple Time Series by the Method of Successive Orthogonalization.

Abstract

This paper concerns the modeling of covariance stationary multiple time series by the method of successive orthogonalization. Fundamental to the method of successive orthogonalization is the factorization of the spectral density matrix which results into a unique triangular two sided moving average model (TTSMA) model). The proposed method of modeling multiple time series is to build (specify, estimate and check) a TTSMA model for a given set of observations. Any other form of the model, say the canonical model, can be obtained by an appropriate transformation.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1976
Accession Number
ADA022742

Entities

People

  • M. S. Phadke

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Acquisition
  • Computing-Related Activities
  • Covariance
  • Data Acquisition
  • Data Science
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Observation
  • Stationary

Fields of Study

  • Engineering
  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Statistical inference.