{"ID":5438832,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T11:37:34.911515878Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31532","arxiv_id":"2606.31532","title":"A time-series classification framework for individual-level absenteeism prediction under severe class imbalance","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 structure inherent in individual attendance histories. We propose a Time Series Classification (TSC) framework that separates historical attendance sequences from future absence labels, enabling genuinely proactive prediction. Due to the lack of public longitudinal attendance data, we construct a reproducible simulated dataset calibrated to the UCI dataset. We analyse Binary Focal Loss (BFL) and Geometric Mean (G-Mean) loss under severe class imbalance using only the imbalance ratio $ρ$. For BFL, the initial gradient ratio is $ρα/(1-α)$, implying the balanced weight $α= 1/(1+ρ) \\approx 0.023$. Experiments show that performance is governed mainly by $α$, with BFL achieving specificity 0.813 and balanced accuracy 0.888, comparable to G-Mean. Unlike BFL, G-Mean adapts automatically without parameter calibration. Among three deep learning architectures evaluated, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and the hybrid LSTM-Fully Convolutional Network (LSTM-FCN), the LSTM-FCN delivers strong precision and specificity. Stable performance is obtained with batch sizes \u003e= 64 and window sizes between 40-80 days, yielding balanced accuracy of approximately 80% on held-out test data.","short_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 classific...","url_abs":"https://arxiv.org/abs/2606.31532","url_pdf":"https://arxiv.org/pdf/2606.31532v1","authors":"[\"Kwong Ho Li\",\"Matthew Roughan\",\"Wathsala Karunarathne\"]","published":"2026-06-30T11:44:52Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}