预测准确率不能可靠判断时间结构，需结合稳定性评估。
When Does Forecasting Reveal Temporal Structure? A Stability Analysis of Time-Series Structural Selection
- 提出稳定性视角，评估预测误差对结构选择的影响。
- 发现微小预测差异仍可揭示真实时间结构。
- 适合关注模型可解释性与结构识别的科研人员。
预测准确性常被用作时间结构发现的代理指标，但预测性能与结构可辨识性并不等价。不同时间机制可能产生相似的预测误差，而微小的预测差异仍可能包含足够的信息用于恢复。本文研究仅基于预测结果进行结构选择的可靠性条件。结果表明，预测误差趋近于零并不必然意味着结构模糊；我们建立了以选择目标不确定性为基准的稳定性分析框架，既能提供可靠的结构选择充分条件，也能量化选择难度。在受控环境与端到端设置中的实验表明，仅依赖预测差距是不足的，而提出的稳定性度量能更准确判断基于预测的结构选择成败。研究建议，只有当预测分离足够稳健时，预测精度才可作为结构发现的证据。
原文摘要 · Abstract (English)
Forecast accuracy is often used as a proxy for temporal structure discovery, but predictive performance and structural identifiability are not equivalent. Different temporal mechanisms can achieve similar forecast errors, while small forecast differences may still contain sufficient information for recovery. In this work, we study when forecast-only structural selection can be trusted. We show that a vanishing forecast margin does not necessarily imply structural ambiguity, and establish a stability perspective that evaluates structural separation relative to uncertainty in the selection objective. This perspective provides both a sufficient condition for reliable selection and a continuous measure of selection difficulty. Experiments across controlled and end-to-end settings demonstrate that forecast margin alone is insufficient, while the proposed stability measure better characterizes when forecast-based structural selection succeeds or fails. Our results suggest that predictive accuracy should be treated as evidence for structure discovery only when its separation is sufficiently robust.
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