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

arXiv:2602.12120v2 Announce Type: replace
Abstract: 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 introduce a leakage-safe covariate protocol incorporating Google Trends proxies and a novel LLM-derived Institutional Operating Conditions Index (IOCI). Using an expanding-window backtest with strict vintage control, we evaluate point accuracy and probabilistic calibration. We find that covariate-conditioned TSFMs perform competitively with classical methods in short samples, though performance varies significantly by model capacity and cohort. We provide an auditable framework for operationalising narrative evidence into exogenous predictors, offering practical guidance for forecasting under data sparsity.