Adaptive Time Series Analysis Using Predictive Inference and Entropy.

Abstract

Research is being conducted on adaptive time series methods for detecting and tracking both abrupt and slow changes in both structure and parameters. The methods are based on a unified statistical frame work which is motivated by statistical inference and entropy arguments. The method yields estimates of input/output dynamics and noise statistics. An integrated approach which combines canonical variates analysis and maximum likelihood estimation has been developed and tested. Specific attention is given to the problem of parameter truncation in both a linear predictor and Kalman filter framework.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA191858

Entities

People

  • Donald E. Dustafson

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computational Science
  • Data Science
  • Detection
  • Detectors
  • Filters
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Maximum Likelihood Estimation
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms