ECoLAD：面向部署的汽车时序异常检测评估框架

摘要

时间序列异常检测器通常在无约束执行的工作站级硬件上进行性能比较。然而，车载监控需要在有限CPU并行度下实现可预测的延迟与稳定行为。仅以准确度排名的评估榜单可能误导性地呈现那些在部署相关约束下仍具可行性的方法。我们提出ECoLAD（异常检测效率计算阶梯），这是一种面向部署的评估方案，具体体现为对专有汽车遥测数据（异常率约0.022%）与互补公共基准的实证研究。该方案通过机械确定的纯整数缩放规则和显式CPU线程限制，在异构检测器家族上应用单调计算缩减阶梯，并记录所有配置变更。通过扫描目标评分速率并报告：（i）覆盖率（达到目标的实体比例），以及（ii）满足目标的阶梯配置中可达到的最佳AUC-PR值，来表征吞吐量约束下的行为特征。在受限的汽车遥测场景中，轻量级经典检测器能在全吞吐量扫描范围内同时维持覆盖率与随机基线以上的检测提升，而若干深度学习方法在保持精度之前已先丧失可行性。

English

Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution. In-vehicle monitoring, however, requires predictable latency and stable behavior under limited CPU parallelism. Accuracy-only leaderboards can therefore misrepresent which methods remain feasible under deployment-relevant constraints. We present ECoLAD (Efficiency Compute Ladder for Anomaly Detection), a deployment-oriented evaluation protocol instantiated as an empirical study on proprietary automotive telemetry (anomaly rate {approx}0.022) and complementary public benchmarks. ECoLAD applies a monotone compute-reduction ladder across heterogeneous detector families using mechanically determined, integer-only scaling rules and explicit CPU thread caps, while logging every applied configuration change. Throughput-constrained behavior is characterized by sweeping target scoring rates and reporting (i) coverage (the fraction of entities meeting the target) and (ii) the best AUC-PR achievable among measured ladder configurations satisfying the target. On constrained automotive telemetry, lightweight classical detectors sustain both coverage and detection lift above the random baseline across the full throughput sweep. Several deep methods lose feasibility before they lose accuracy.

ECoLAD：面向部署的汽车时序异常检测评估框架

ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection

摘要

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