发表机构
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出动力学参数框架，揭示时间序列基础模型中隐藏状态可获取的参数信息未必在预测中体现，通过因果几何分析解释该差距，并指出输入变化方向是关键因素。
AI 中文摘要
本研究探讨了时间序列基础模型（TSFMs）解释中的一个核心空白：即使预测未能随动力学属性的变化而正确响应，该属性仍可能在隐藏状态中被获取。我们将这些属性形式化为动力学参数，包括趋势斜率、振荡频率和自回归依赖性。我们比较了这些参数的表示可获取性（通过从隐藏状态中恢复来度量）与预测响应（通过与预期预测变化的一致性来度量）。在九个冻结的TSFMs和十三条定律中，63个模型-参数单元中有42个实现了高于0.95的可获取性，而它们相对于条件参考的中位参考对齐响应仅为0.46。为解释这一差距，因果几何比较了产生参考响应所需的隐藏状态变化与参数干预引起的变化。直接修改隐藏状态可以恢复参考响应，但参数干预往往使状态向不同方向移动。这些结果表明，当输入变化未命中所需的隐藏状态方向时，可获取的参数信息不一定会体现在预测中。
英文摘要
This work studies a central gap in interpreting time-series foundation models (TSFMs): a dynamical property may be accessible in a hidden state even when the forecast fails to respond correctly as that property changes. We formalize these properties as Dynamical Parameters, including trend slope, oscillation frequency, and autoregressive dependence. We compare their representation accessibility, measured by recovery from hidden states, with their forecast response, measured by agreement with the expected forecast change. Across nine frozen TSFMs and thirteen laws, 42 of 63 model-parameter cells achieve accessibility above 0.95, whereas their median reference-aligned response relative to the conditional reference is only 0.46. To explain this gap, causal geometry compares the hidden-state change required to produce the reference response with the change induced by the parameter intervention. Directly modifying the hidden state recovers the reference response, but the parameter intervention often moves the state in a different direction. These results show that accessible parameter information need not be expressed in forecasts when input changes miss the required hidden-state direction.
