ECoLAD shows classical anomaly detectors maintain coverage and accuracy lift under automotive compute limits while several deep methods lose feasibility first.
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
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ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection
ECoLAD shows classical anomaly detectors maintain coverage and accuracy lift under automotive compute limits while several deep methods lose feasibility first.