AI RESEARCH
ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection
arXiv CS.LG
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ArXi:2603.10926v1 Announce Type: new 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.