Anomaly Detection in Time-series Employee Absence Data: A Case Study
Peter Zupančič, Panče Panov · 2024
This paper provides an initial exploration of employee absence data for anomaly detection. Utilizing data collected from the MojeUre system, which aggregates employee data from diverse companies, our objective is to uncover hidden patterns and anomalies associated with absences. In this paper, we employ various anomaly detection techniques to identify and characterize unusual patterns in absence data. The comparative analysis in this paper offers valuable initial insights for organizations aiming to leverage data analytics for workforce management and strategic decision-making, particularly in the context of anomaly detection.