Time Series Model Identification by Estimating Information.

Abstract

Statisticians, economists, and system engineers are becoming aware that to identify models for time series and dynamic systems, information theoretic ideas can plan a valuable (and unifying) role. This paper discusses how models for a univariate or multivariate time series Y(t) can be formulated as hypotheses about the information divergence between alternative models for the conditional probability density of Y(t) given various bases involving past, current, and future values of Y(.) and related time series x(.). To determine sets of variables that are sufficient to forecast Y(t), and thus to determine a model for Y(t), an approach is presented which estimates and compares various information increments. These information numbers play a central role in studies of causality and feedback. Approximating autoregressive schemes are used to form estimators of the many information numbers that one might compare to identify models for a time series.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1982
Accession Number
ADA129725

Entities

People

  • Emanuel Parzen

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Estimators
  • Feedback
  • Hypotheses
  • Information Science
  • Multivariate Analysis
  • Normal Distribution
  • Probability
  • Random Variables
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Time Series Analysis
  • White Noise

Fields of Study

  • Mathematics

Readers

  • International Relations and European Studies
  • Regression Analysis.
  • Systems Analysis and Design