A time-series classification framework for individual-level absenteeism prediction under severe class imbalance

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 futu

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ethics.ai (30 June 2026), “A time-series classification framework for individual-level absenteeism prediction under severe class imbalance,” evidence record 402, https://ethics.ai/record/402 (originally published by arXiv).

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