Commencing-Student Enrolment Forecasting Under Data Sparsity with Time Series Foundation Models
This addresses financial planning challenges for universities by improving enrollment forecasts in data-sparse environments, though it is incremental as it builds on existing TSFM methods.
The paper tackled the problem of forecasting university commencing-student enrollments under data sparsity by using time series foundation models (TSFMs) in a zero-shot setting, achieving performance on par with classical benchmarks without institution-specific training, with results varying by cohort and model.
Many universities face increasing financial pressure and rely on accurate forecasts of commencing enrolments. However, enrolment forecasting in higher education is often data-sparse; annual series are short and affected by reporting changes and regime shifts. Popular classical approaches can be unreliable, as parameter estimation and model selection are unstable with short samples, and structural breaks degrade extrapolation. Recently, TSFMs have provided zero-shot priors, delivering strong gains in annual, data-sparse institutional forecasting under leakage-disciplined covariate construction. We benchmark multiple TSFM families in a zero-shot setting and test a compact, leakage-safe covariate set and introduce the Institutional Operating Conditions Index (IOCI), a transferable 0-100 regime covariate derived from time-stamped documentary evidence available at each forecast origin, alongside Google Trends demand proxies with stabilising feature engineering. Using an expanding-window backtest with strict vintage alignment, covariate-conditioned TSFMs perform on par with classical benchmarks without institution-specific training, with performance differences varying by cohort and model.