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Predicting employee attrition with a more effective use of historical events

Abstract

Attrition prediction research typically focuses on constructing models that involves one observation per employee over a limited time period, while the rest of the employees are discarded. Time-series attributes are transformed to non-time-series ones by applying statistical operations (e.g. sum, max, etc.). Such methods result in information loss and therefore less effective predictions. In this paper, we introduce a dynamic approach to employee attrition prediction, leveraging the longitudinal nature of the data, and allowing the models to generalize across behaviors and providing a closer estimate of the employee risk of leaving.
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Dates and versions

hal-03697870 , version 1 (17-06-2022)

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Abdel-Rahmen Korichi, Hamamache Kheddouci, Daniel J West. Predicting employee attrition with a more effective use of historical events. Oct 2021, Bruges (on line), Belgium. pp.53-58, ⟨10.14428/esann/2021.ES2021-110⟩. ⟨hal-03697870⟩
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