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

TL;DR

ECoLAD introduces a deployment-oriented evaluation protocol for automotive time-series anomaly detection, emphasizing latency and stability constraints often overlooked by accuracy-only benchmarks.

Contribution

The paper presents ECoLAD, a novel evaluation framework that assesses anomaly detectors under realistic deployment constraints using a compute-reduction ladder and explicit CPU caps.

Findings

Lightweight classical detectors maintain coverage and detection performance under constraints.

Deep methods often become infeasible before their accuracy drops.

ECoLAD effectively characterizes throughput-constrained behavior of detectors.

Abstract

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.