The Spectrum Is Not Enough: When Context Helps Time-Series Forecasting
Score
76该论文纠正了常见误解，区分频谱与上下文价值，提供新诊断指标，信息增量较大。
论证严谨，使用受控实验和统计检验，理论分析扎实，内容质量高。
诊断指标可直接用于系统中判断是否引入上下文，但需进一步验证其实用性。
近期arXiv预印本，讨论当前时间序列预测中检索和基础模型的热点问题，时效性强。
来源为arXiv预印本，提供匿名代码可复现，但未经同行评审，信源可靠度较高。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
It innovatively distinguishes spectrum from context value and provides an actionable diagnostic, offering significant guidance for time-series forecasting practice.
AI commentary
「This paper timely corrects a common misuse of spectral indices in the industry. For engineers and researchers working on time-series forecasting, understanding that context value cannot be inferred from spectrum alone is critical; a more nuanced diagnostic is needed. The coverage deficit could become a practical tool, especially for rapid assessment before deploying retrieval-augmented or foundation models.」
Quick overview
This paper argues that common spectral indices cannot measure the value of context (e.g., longer lookback, retrieval plug-in, pretrained models) for time-series forecasting, because context value depends on the operating point, not the series itself. By constructing surrogate pairs via phase randomization that preserve the spectrum, the authors show that spectral indices are invariant to higher-order structure. They propose a label-free diagnostic, the coverage deficit, which measures beyond-spectrum structure as the gain of analog over linear prediction. On seven benchmarks, window-keyed retrieval's value collapses across surrogates (ECL median from +33% to -35%, p<10^{-40}) while all spectral indices remain frozen; a foundation model's value partially survives; the structure term predicts the sign of beyond-spectrum value. The paper contributes a distinction, a controlled comparison, and a deployment diagnostic without introducing new forecasters.
- Spectral indices cannot answer whether adding context helps, because context value depends on the operating point.
- Phase randomization preserves the spectrum but destroys higher-order structure; spectral indices remain unchanged while retrieval/foundation model values collapse.
- The paper proposes coverage deficit, a label-free diagnostic measuring beyond-spectrum structure as the gain of analog over linear prediction.
- Experiments show that window-keyed retrieval's value drops from +33% to -35% across surrogates, while all spectral indices freeze; foundation model values partially survive.
Full text
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