Abstract
This paper uses a hybrid SARIMA system and an adaptive neuro-fuzzy inference system (ANFIS) to analyze data on interactions between employees in a real-world entity, including email exchanges, chat messages from meetings, and in-person meetings, for the purpose of detecting position changes such as promotions, demotions, and supervisor changes. The dataset, comprising approximately 184 GB of textual data, includes sixteen features related to employee interactions, such as internal contacts, communication with supervisors, subordinates, and individuals at various levels of the hierarchy. The developed system achieved a detection accuracy of 96%, confirming its usefulness in monitoring personnel processes and optimizing human resource management. In this study, the SARIMA model was combined with the ANFIS system, enabling more precise forecasting of changes over time and the detection of employee behaviors, such as sudden position changes or team interactions. By operating in a quasi-real-time mode, the system allows for the rapid identification of potential irregularities, enhancing organizational security and supporting personnel decision-making in dynamically changing conditions. The results of our research indicate that hybrid models integrating the analysis of large datasets and flexible inference systems can effectively support management and behavioral profiling in organizations.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.91
Application of Hybrid Systems SARIMA ANFIS for Monitoring Workforce Dynamics
This paper uses a hybrid SARIMA system and an adaptive neuro-fuzzy inference system (ANFIS) to analyze data on interactions between employees in a real-world entity, including email exchanges, chat messages from meetings, and in-person meetings, for the purpose of detecting position changes such as promotions, demotions, and supervisor changes. The dataset, comprising approximately 184 GB of textual data, includes sixteen features related to employee interactions, such as internal contacts, communication with supervisors, subordinates, and individuals at various levels of the hierarchy. The developed system achieved a detection accuracy of 96%, confirming its usefulness in monitoring personnel processes and optimizing human resource management. In this study, the SARIMA model was combined with the ANFIS system, enabling more precise forecasting of changes over time and the detection of employee behaviors, such as sudden position changes or team interactions. By operating in a quasi-real-time mode, the system allows for the rapid identification of potential irregularities, enhancing organizational security and supporting personnel decision-making in dynamically changing conditions. The results of our research indicate that hybrid models integrating the analysis of large datasets and flexible inference systems can effectively support management and behavioral profiling in organizations.
Recommended Citation
Nowak, J., Korytkowski, M., Scherer, R., Żak, B., Tymorek, Z. & Zbieg, A. (2025). Application of Hybrid Systems SARIMA ANFIS for Monitoring Workforce DynamicsIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.91