The Finite Memory Prediction of Covariance Stationary Time Series.

Abstract

An algorithm is presented for conveniently calculating h step ahead minimum mean square linear predictors and prediction variances given a finite number of observations from a covariance stationary time series Y. It is shown that elements of the modified Cholesky decomposition of the covariance matrix of observations play the role in finite memory prediction that the coefficients in the infinite order moving average representation of Y play in infinite memory prediction. The algorithm is applied to autoregressive-moving average time series where further simplifications are shown to occur. A numerical example illustrating the basic points of the general algorithm is presented. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1981
Accession Number
ADA105413

Entities

People

  • H. J. Newton
  • Marcello Pagano

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Coefficients
  • Computer Programs
  • Convergence
  • Covariance
  • Data Analysis
  • Data Science
  • Decomposition
  • Information Science
  • Military Research
  • New York
  • Observation
  • Stationary
  • Statistical Data
  • Statistics
  • Time Series Analysis
  • Universities

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Parallel and Distributed Computing.
  • Statistical inference.