Kalman and Moving Average Filters for Forecasting: Systematics of Demand Processes and Extensions.

Abstract

This technical report is a digest of theoretical (mostly) results on short term forecasting. The ubiquitous criterion is mean square error MSE, which is the most amenable to mathematical analysis, but in some transformations of process variables to be forecasted infer other error measures. This study is also a proselytization for the Kalman filter, which together with its underlying vector process model, is a powerful and flexible algorithm which encompasses many other techniques. To an extent, the study concerns systematics, a scheme for classifying such well known forecasting algorithms as exponential smoothing, moving averages, regression line fitting, by the underlying processes for which these algorithms are optimal or sub-optimal.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1976
Accession Number
ADA032496

Entities

People

  • Donald A. Orr

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Computer Science
  • Delphi Method
  • Engineering
  • Estimators
  • Frequency Domain
  • Industrial Engineering
  • Kalman Filters
  • Logistics
  • Logistics Management
  • Management Engineering
  • Mathematical Analysis
  • Operations Research
  • Regression Analysis
  • Steady State

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation

Technology Areas

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