这篇论文澄清了一个常见误解：别拿频谱指标来判断加上下文有没有用。作者给出了新诊断方法，实验做得扎实。
该论文指出，基于频谱的可预测性分数不能回答'添加上下文（如更长回看窗口、检索插件或预训练模型）是否有帮助'的问题。频谱指标在相位随机化下不变，而上下文价值依赖操作点，并非序列固有属性。作者提出无标签的配置级诊断'覆盖亏缺'（coverage deficit），其主项衡量超越频谱的结构增益。在7个基准上验证：基于窗口的检索价值在代理对中从中位数+33%降至-35%（p<10^{-40}），而频谱指标不变；基础模型的价值仅有小的超越线性部分。
The Spectrum Is Not Enough: When Context Helps Time-Series Forecasting
A growing family of indices scores how predictable a series is from its spectrum. Practitioners increasingly read these scores as answering a different question: whether \emph{adding context}, a longer lookback, a retrieval plug-in, or a pretrained model, will help. These are not the same question. The value of context is a property of the operating point, not of the series. Any index built from the power spectrum is invariant under phase randomization, whereas the beyond-second-order value that retrieval and foundation models supply is not, because a phase-randomized series is asymptotically Gaussian. We state this as an impossibility result and isolate it with surrogate pairs that fix the spectrum and the marginal by construction. We then give a label-free, configuration-level diagnostic, the coverage deficit, whose principal term measures beyond-spectrum structure as the gain of analog over linear prediction. On seven benchmarks the prediction holds: window-keyed retrieval's value collapses across surrogate pairs (ECL median $+33\%\!\to\!-35\%$, $p{<}10^{-40}$) while every spectral index stays frozen; a foundation model's value splits into a surviving second-order part and a small beyond-linear margin that collapses; a longer linear window's value survives. Leave-one-dataset-out, the structure term predicts the sign of beyond-spectrum value where the spectral indices trail it, and the reverse holds for the second-order mechanism. We introduce no new forecaster; the contribution is the distinction, a controlled comparison, and a diagnostic for the deployment decision. Code: https://anonymous.4open.science/r/SINE.