FORECASTING EMPLOYEE PRODUCTIVITY USING LSTM NETWORKS: SYNTHETIC DATA APPROACH AND BASELINE COMPARISON
Abstract
Forecasting employee productivity is becoming an important task in modern project management environments characterized by high dynamics and digital transformation. The aim of this study is to develop and investigate an approach for predicting employee productivity using Long Short-Term Memory (LSTM) neural networks. The methodology is based on modeling temporal sequences of HR metrics describing employee activity and performance in project teams. The results demonstrate that LSTM models can effectively capture temporal dependencies in productivity indicators and provide accurate forecasts of performance dynamics. The practical value of the study lies in the possibility of integrating such models into HR analytics and project management systems to support performance monitoring and managerial decision-making. In addition, the proposed approach can contribute to improving resource planning and early identification of changes in team performance.
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