arXiv：cs.LG· Hong-In Won·· 4 小时前AI 评分43
时间序列基础模型 Chronos-2、TimesFM-2.5 与 TabPFN-TS 的上下文辨识衰减：反事实输入诊断与合成强制系统微调修复
Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning
AI 导读
研究用具有精确反事实的强制工程系统测试了 Chronos-2、TimesFM-2.5 和 TabPFN-TS 的上下文辨识能力，发现 TimesFM-2.5 与 TabPFN-TS 通过默认协变量接口表现无记忆，TimesFM-2.5 的预测效应与输入变化同时刻函数拟合度达 R²=1.000。
正文
Abstract:Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a different future input would cause. We test this on forced engineering systems with exact counterfactuals, comparing Chronos-2, TimesFM-2.5 and TabPFN-TS with classical system identification fitted to the same context. Through their default covariate interfaces, TimesFM-2.5 and TabPFN-TS are memoryless: the predicted effect of an input change is a same-time function of that change ($R^2 = 1.000$ for TimesFM-2.5). Chronos-2 identifies dynamics in context but attenuates them. Its predicted effect is 0.33-0.80 of the true effect, its recovered impulse response has the wrong shape, and its error on a one-degree-of-freedom oscillator levels off at 0.57 with 8192 context samples, where ARX fitted to 256 samples reaches 0.02. Context dither at inference lowers the what-if error on all six synthetic classes without training. A 26-minute fine-tune on synthetic forced systems restores the response magnitude (sensitivity 0.83-0.96) and outperforms structure-agnostic identification on Wiener-Hammerstein and a held-out friction class. A specialised in-context identifier trained on the same data comes close, so the forced-system data carry most of the gain. On three of four measured plants classical identification remains clearly better, and the fine-tuned model loses part of its univariate forecasting skill. Paired counterfactual inputs, together with shuffled future inputs on measured records, test two properties: whether the covariate interface can represent dynamics and whether the pretraining prior covers the plant's time scale. Only the counterfactual pairs expose the attenuation.
| Comments: | 12 pages, 4 figures, 5 tables |
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.08118 [cs.LG] |
| (or arXiv:2610.08118v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08118 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hong-In Won Dr. [view email]
[v1]
Tue, 6 Oct 2026 10:39:04 UTC (120 KB)
来源：arXiv：cs.LG · arxiv.org