Zero-Shot Time Series Foundation Models for Annual Institutional Forecasting Under Data Sparsity

ArXi:2602.12120v2 Announce Type: replace Forecasting annual institutional demand is notoriously difficult due to data sparsity, reporting changes, and regime shifts. Traditional baselines often falter under these low signal-to-noise conditions, yet sample sizes are too small for complex parameterised models. We benchmark zero-shot Time Series Foundation Models (TSFMs) against classical persistence and ARIMA baselines for annual enrolment forecasting. To address structural breaks without look-ahead bias, we.