发表机构
Deakin University; Deakin Applied Artifical Intelligence Initiative（迪肯大学; 迪肯大学应用人工智能倡议）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究时间序列基础模型中专家指导预测编辑问题，提出DEFT框架，先利用基础模型预测样本，再逐分量细化探索，仅对完整轨迹查询专家并重用分数，在多数据集和模型等设置下，能有效提高专家指导的有效性。
AI 中文摘要
时间序列基础模型可跨异构领域预测且无需特定任务训练，但预测结果一旦生成便固定，无法直接纳入特定任务专家反馈。本文研究专家指导预测编辑，冻结的基础模型生成候选未来轨迹，昂贵的专家评估器对其评分以指导预测修正。在严格查询预算下，两种自然策略各有弊端。为此引入DEFT框架，先在分解的趋势-季节空间利用基础模型的预测样本，再通过逐分量细化进行探索。该框架仅对完整轨迹查询专家，重用查询重组中出现的趋势和季节分量的分数。实验表明，DEFT在匹配的专家查询预算下，始终能提高专家指导的有效性。分子动力学案例研究表明该原理也适用于更基于物理的反馈。
英文摘要
Time-series foundation models can forecast across heterogeneous domains without task-specific training, but their forecasts are fixed once produced and cannot directly incorporate task-specific expert feedback. We study expert-guided forecast editing: a frozen foundation model generates candidate future trajectories, and an expensive expert evaluator scores them to guide forecast revision. Under a tight query budget, two natural strategies sit at opposite ends: best-of-$N$ purely exploits the foundation model's predictive distribution, while optimization approaches mostly explore the forecast horizon as an unstructured high-dimensional vector. Each extreme is individually sub-optimal. We introduce \textbf{DEFT}, an expert-guided forecast editing framework that balances the two by first exploiting the foundation model's predictive samples in a decomposed trend--seasonal space, then exploring around them via component-wise refinement. DEFT queries the expert only on complete trajectories, then reuses scores for the trend and seasonal components that appeared in the queried recombinations. This lets each expert query provide structured component-level feedback while keeping the foundation model frozen. We compare DEFT against direct search approaches, including best-of-$N$, cross-entropy methods, and Bayesian optimization, under matched expert-query budgets. Across two forecasting benchmarks consisting of 78 datasets, three time-series foundation models, four feedback types, and seven query budgets, DEFT consistently improves the effectiveness of expert guidance. A molecular-dynamics case study further suggests that the same principle extends to more physically grounded feedback, supporting the hypothesis that sparse test-time guidance should be spent balancing prior exploitation with structured exploration.
Commentspreprint 34 pages
