Date of Award

3-2022

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Raymond R. Hill, PhD

Abstract

Officer retention in the Air Force has been researched many times in an attempt to better predict the personnel needs of the Air Force for the future. There has been previous work done in regards to specific AFSCs and how their retention compares to specific yet similar private sector jobs. This study considers different econometric time series statistics as a feature space and an average Air Force officer separation rate as the response variable for the multivariate time series analysis deep learning techniques. The econometric indicators used in this study are New Business Formations, New Durable Good Orders, and the Consumer Confidence Index. The techniques considered for this study were Long Term Short Memory (LSTMs) Networks and Gated Recurrent Unit (GRU) Networks. This study shows that both GRUs and LSTMs perform fairly well with a forecast of 14 months out, but does not perform well comparatively to the more traditional univariate time series forecasting techniques, ARIMA models. The career fields with better performing models were career fields that will have jobs outside of the Air Force that will be more likely to hire in a period of economic growth, which would in turn increase the separation rate.

AFIT Designator

AFIT-ENS-MS-22-M-159

DTIC Accession Number

AD1172383

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