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.

翻译：许多高校面临日益增长的财务压力，依赖准确的新生入学人数预测。然而，高等教育领域的入学预测常面临数据稀疏问题：年度序列较短，且受报告方式变更与体制变迁的影响。流行的经典方法可能不可靠，因为参数估计和模型选择在短样本下不稳定，结构突变会降低外推性能。近期，时间序列基础模型为零样本预测提供了先验知识，在严格防止数据泄露的协变量构建框架下，为年度、数据稀疏的机构预测带来了显著提升。我们在零样本设定下对多个时间序列基础模型族进行基准测试，验证了一套紧凑且防泄露的协变量集，并引入了机构运营状况指数——一种可迁移的0-100体制协变量，该指数源自各预测时点可获取的时间戳文档证据，同时结合经过特征工程稳定的谷歌趋势需求代理变量。通过采用严格版本对齐的扩展窗口回测，在未经机构特定训练的情况下，协变量条件化的时间序列基础模型表现与经典基准方法相当，其性能差异因入学批次和模型类型而异。