Reversed Residuals in Autoregressive Time Series Analysis

Abstract

Both linear and nonlinear time series can have directional features, features which indicate that the series do not maintain identical statistical properties when the direction on the time scale is reversed. The main purpose of the present paper is to develop the analysis of these features and to indicate and illustrate how they can be used for the investigation and modelling of linear or nonlinear autoregressive statistical models. In particular, the aim of the paper is to introduce the idea of reversed residuals and to develop some of their properties. Particular pairs of reversed and ordinary residuals are shown to produce partial autocorrelation coefficients: quadratic types of partial autocorrelation coefficients are introduced to assess dependence associated with nonlinear models which nevertheless have linear autoregressive (Yule-Walker) correlation structures. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1990
Accession Number
ADA222711

Entities

People

  • A. J. Lawrance
  • Peter A. Lewis

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Autocorrelation
  • Coefficients
  • Computational Science
  • Cross Correlation
  • Data Analysis
  • Data Science
  • Data Sets
  • Information Science
  • Linearity
  • Military Research
  • Models
  • Operations Research
  • Quadrants
  • Random Variables
  • Residuals
  • Standards
  • Time Series Analysis

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.