发表机构
University of Freiburg; Norwegian University of Science and Technology（弗莱堡大学; 挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究时间序列基础模型用于预测控制时，上下文中的控制激励对准确预测系统响应的重要性，发现足够的激励是必要的，并初步验证了短窗口的闭环可行性。
AI 中文摘要
部署模型预测控制（MPC）需要为每个目标系统构建或识别预测模型。时间序列基础模型（TSFMs）凭借跨系统的强大零样本预测能力，提供了一个有吸引力的选择。然而，低预测误差并不能保证TSFM捕捉到系统对控制器所考虑的替代动作的响应。我们以住宅热泵控制为测试平台，研究这一差距，衡量控制干预的预测效果与真实效果之间的一致性。重要的是，我们发现当上下文包含足够的独立控制激励时，TSFM能够恢复系统的输入-响应关系。常见的微调流程和特征平滑会减少但不会消除对上下文内激励的需求。我们的结果表明，当前用于预测控制的TSFM需要推理上下文中有足够信息量的控制变化。初步的闭环结果显示出在较短上下文窗口下的前景。
英文摘要
Deploying model predictive control (MPC) requires constructing or identifying a predictive model for each target system. Time-series foundation models (TSFMs) offer an attractive option thanks to strong zero-shot forecasting capabilities across systems. However, low forecast error does not guarantee that a TSFM captures the system's response to the alternative actions considered by the controller. We study this gap using residential heat-pump control as a test bed, measuring the agreement between predicted and ground-truth effects of control interventions. Importantly, we find that TSFMs can recover the system's input-response relationship when the context contains sufficient independent control excitation. Common fine-tuning pipelines and feature smoothing reduce, but do not eliminate, the need for in-context excitation. Our results indicate that current TSFMs used for predictive control require sufficiently informative control variation in the inference context. Initial closed-loop results show promise for shorter context windows.
CommentsAccepted at the TS-LIMITS Workshop at NeurIPS 2026
