Forecasting with Machine Learning: DATAWorks 2022

Abstract

The Department of Defense (DoD) has a considerable interest in forecasting key quantities of interest including demand signals, personnel flows, and equipment failure. Many forecasting tools exist to aid in predicting future outcomes, and there are many methods to evaluate the quality and uncertainty in those forecasts. When used appropriately, these methods can facilitate planning and lead to dramatic reductions in costs. This talk explores the application of machine learning algorithms, specifically gradient-boosted tree models, to forecasting and presents some of the various advantages and pitfalls of this approach. We conclude with an example where we use gradient-boosted trees to forecast Air National Guard personnel retention.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2022
Accession Number
AD1210692

Entities

People

  • Akshay A. Jain
  • John W. Dennis

Organizations

  • Institute for Defense Analyses

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Air National Guard
  • Aircraft Equipment
  • Aircrafts
  • Case Studies
  • Delphi Method
  • Department Of Defense
  • Dimensionality Reduction
  • Labor Markets
  • Learning
  • Machine Learning
  • Military Personnel
  • National Guard
  • Neural Networks
  • Personnel Retention
  • Precision
  • Survival
  • Training
  • Uncertainty
  • Virginia

Fields of Study

  • Computer science

Readers

  • Geodesy
  • Naval Personnel Management
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks