零样本时间序列模型在数据稀疏下仍可精准预测高校入学人数
Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning
- 用零样本时间序列基础模型应对数据稀缺的入学预测难题
- 结合谷歌趋势与机构运营指数，提升模型在结构变化中的预测准确率
- 为高校管理者提供可审计、可迁移的决策支持框架
高校资源分配依赖可靠的入学人数预测，但规划者常面临因结构性变化导致的数据中断问题。本文探讨零样本时间序列基础模型（TSFMs）在严重数据稀疏条件下是否能为年度入学预测提供可靠决策支持。我们通过扩展窗口回测，将多种TSFMs与经典基准方法对比。为避免信息泄露，引入安全协变量协议，整合特征工程后的谷歌趋势与机构运营条件指数（IOCI），该指数从历史文本证据中提取，具有可迁移性。评估表明，协变量条件化的TSFMs在不需特定机构训练的情况下，表现可媲美传统方法，并提升预测精度。但实际效益取决于生源特征和协变量设计。本研究为研究人员和高校管理者提供了可审计、可迁移的预测协议，帮助判断在数据有限与结构不稳定时，上下文感知预测是否具备实用价值。
原文摘要 · Abstract (English)
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.
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