ECoLAD：面向部署的汽车时序异常检测评估体系

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和稳定行为。仅以精度为导向的排行榜可能误导性地呈现哪些方法在符合部署约束条件下仍具可行性。我们提出ECoLAD（异常检测效率计算阶梯），这一面向部署的评估协议通过对专有汽车遥测数据（异常率约0.022%）和互补公共基准的实证研究实现。ECoLAD采用机械确定的纯整数缩放规则和显式CPU线程限制，在异构检测器家族中应用单调计算资源缩减阶梯，并记录所有配置变更。通过扫描目标评分速率并报告以下指标来表征吞吐量约束行为：(i)覆盖率（达到目标的实体比例），以及(ii)在满足目标的阶梯配置中可达到的最佳AUC-PR值。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维持覆盖率及超越随机基线的检测提升能力，而部分深度学习方法在保持精度的前提下已丧失可行性。

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体系

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

摘要

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