# ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation

arXiv: https://arxiv.org/abs/2603.10926 · HTML: https://mlsanity.com/paper/2603.10926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [TAB: Unified Benchmarking of Time Series Anomaly Detection Methods](https://mlsanity.com/paper/2506.18046/) (2025)
- [TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models](https://mlsanity.com/paper/2402.10802/) (2024)

## Abstract (arXiv)

Automotive anomaly detectors are often selected from accuracy only benchmarks on workstation class hardware, whereas in-vehicle monitoring requires predictable scoring latency under limited CPU parallelism. This mismatch can make methods that appear competitive offline infeasible for deployment. We present ECoLAD (Efficiency Compute Ladder for Anomaly Detection), a deployment-oriented evaluation protocol for automotive time-series anomaly detection (TSAD). ECoLAD defines a monotone compute reduction ladder with explicit CPU thread caps, mechanical integer only hyperparameter scaling, inference/full run timing separation, and auditable run logs. Applied to proprietary in-vehicle telemetry and two public benchmarks, it shows that accuracy stability and deployment feasibility can diverge: some deep detectors retain AUC-PR while losing feasible throughput, whereas lightweight classical detectors sustain high scoring rates with positive lift above the random baseline, providing a practical screening procedure for detector selection under deployment relevant constraints.