Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning

TL;DR

This paper explores the use of zero-shot Time Series Foundation Models for accurate enrolment forecasting in higher education amid data sparsity and structural shifts, proposing a novel covariate protocol.

Contribution

It introduces a leakage-safe covariate protocol combining Google Trends and institutional data, enabling TSFMs to perform well without bespoke training under challenging conditions.

Findings

Covariate-conditioned TSFMs are competitive with classical methods.

The proposed protocol improves forecasting accuracy under data sparsity.

Operational benefits vary based on cohort and covariate design.

Abstract

Effective resource allocation in higher education depends on reliable enrolment forecasts, yet institutional planners frequently face data series disrupted by structural shifts. This paper investigates whether zero-shot Time Series Foundation Models (TSFMs) can provide rigorous decision support for annual enrolment forecasting under severe data sparsity. We benchmark multiple TSFMs against classical operational baselines using an expanding-window backtest that mirrors decision-time constraints. To capture environmental shifts without look-ahead bias, we introduce a leakage-safe covariate protocol that integrates feature-engineered Google Trends with the Institutional Operating Conditions Index (IOCI), a transferable regime measure extracted from historical narrative evidence. Our evaluation demonstrates that covariate-conditioned TSFMs are competitive with classical methods and can improve accuracy without requiring bespoke institutional training. However, the operational benefits depend on cohort characteristics and covariate design. This study provides an auditable and transferable forecasting protocol for operational researchers and university administrators, helping institutions determine when context-aware forecasting adds practical value under limited data and structural instability.