A time-series classification framework for individual-level absenteeism prediction under severe class imbalance
arXiv:2606.31532v1 Announce Type: new Abstract: Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce planning depends on reliable individual-level absence prediction. Existing regression and classification approaches share a structural limitation; they map features observed at time t to labels at the same time t, reproducing already-realised outcomes rather than predicting future events, and discard the sequential behavioural st
The increasing availability of complex time-series data and advancements in AI/ML techniques are enabling more sophisticated predictive models for workforce management.
Reliable individual-level absence prediction can significantly reduce operational costs and improve proactive workforce planning in critical, high-demand sectors.
Traditional reactive approaches to absenteeism are now being challenged by predictive frameworks that can anticipate future events, shifting from 'what happened' to 'what will happen'.
- · Healthcare sector
- · Emergency services
- · Logistics companies
- · HR tech companies
- · Inefficient workforce management systems
- · Companies with high absenteeism rates
- · Sectors reliant on reactive staffing
Widespread adoption of AI-driven absenteeism prediction tools across high-demand industries.
Improved operational efficiency and reduced labor costs for businesses, potentially leading to increased profits or reallocated resources.
Ethical considerations and regulatory frameworks emerging around individual-level predictions and potential biases in AI deployment for workforce management.
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Read at arXiv cs.AI