TRACE-C模型能够对多流遥测进行秩校准关系异常检测，与传统的基于时间通道的检测方法相比，其在某些事件上的排名更为准确。
TRACE-C是一种可审计的严格优先秩校准检测器，用于对齐的多流遥测：同一制度滚动中值/MAD残差为三个窗口通道提供输入，包括最大归一化局部和、基于鲁棒-z残差的Gaussian copula形式依赖对比以及最差标准化AR(1)创新。使用2019年1月至4月的英国电网六条流进行评估，2020年无窗口被选中，表明记录规则饱和而非平淡无奇的一年。
TRACE-C: Rank-Calibrated Relational Anomaly Detection for Multi-Stream Operational Telemetry
Operational telemetry can be jointly anomalous while every individual stream stays inside its familiar range. TRACE-C is an auditable strictly-prior rank-calibrated detector for aligned multi-stream telemetry: same-regime rolling median/MAD residuals feed three window channels -- a maximum normalized local sum, a Gaussian copula-form dependence contrast on robust-z residuals, and a worst standardized AR(1) innovation -- whose channel ranks are Fisher-aggregated and ranked against earlier aggregates. We evaluate six Great Britain grid streams with a January-April 2019 fit, July-December 2019 development evidence, and a 2020 hold-out frozen before inspection. TRACE-C ranks Storm Atiyah first among 2019 test windows, but a disclosed channel ablation attributes that rank to the local channel, not the copula-form channel: copula-only ranks Atiyah 59th. The short 9 August frequency event is ranked far lower by the fused detector (143) than by the temporal channel alone (40), and reconstruction baselines rank it first. In 2020 no window is selected, which is consistent with record-rule saturation rather than an uneventful year; the highest-ranked frozen window was later interpreted as Storm Ellen. Three interpretive limits carry throughout. The resulting p-values are selection quantities, not event probabilities. The copula-form channel is not a literal copula density: the method applies no probability-integral or normal-score transform. Empirical rank counts are diagnostics, not coverage or false-discovery proofs. Every table and figure in this paper is generated from committed machine-readable reports.