Adaptive Time Series Analysis Using Predictive Inference and Entropy

Abstract

Research is reported on adaptive time series methods for detecting and tracking both abrupt and slow changes in both structure and parameters of dynamic systems. The methods are based on a unified statistical framework which is motivated by statistical inferences and entropy arguments. The method yields estimates of multivariate input/output dynamics and noise statistics. It also gives estimate of system order that is optimal in the sense of an information theoretic criterion. The integrated approach is known as CVA-AIC. Many theoretical issues have been explored under the scope of this project. The relationship between this technique and another powerful framework for estimation known as E-M algorithmic approach has been established. It the CVA- AIC technique is embedded properly in an E-M framework, it leads to maximum likelihood estimates and recursive algorithms for system identification. (jhd)

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Document Details

Document Type
Technical Report
Publication Date
Feb 13, 1990
Accession Number
ADA222337

Entities

People

  • Raman K. Mehra
  • Shah Mahmood

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Science
  • Detectors
  • Information Processing
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Mathematical Filters
  • Maximum Likelihood Estimation
  • Random Variables
  • Stationary Processes
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Surveys

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference