ECoLAD shows classical anomaly detectors maintain coverage and accuracy lift under automotive compute limits while several deep methods lose feasibility first.
Tranad: Deep transformer networks for anomaly detection in multivariate time series data
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 3years
2026 3roles
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baseline 1representative citing papers
A transformer model guided by a causal graph prior achieves state-of-the-art anomaly detection and root-cause attribution on ASD and SMD benchmarks by restricting main predictions to graph-supported causes while using an isolated shadow path for residual correlations.
U²AD learns unified normal data representations via score-based generative modeling and a novel time-dependent score network to outperform prior methods in accuracy and early anomaly detection for multivariate time series.
citing papers explorer
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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.
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Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection
A transformer model guided by a causal graph prior achieves state-of-the-art anomaly detection and root-cause attribution on ASD and SMD benchmarks by restricting main predictions to graph-supported causes while using an isolated shadow path for residual correlations.
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Learning Unified Representations of Normalcy for Time Series Anomaly Detection
U²AD learns unified normal data representations via score-based generative modeling and a novel time-dependent score network to outperform prior methods in accuracy and early anomaly detection for multivariate time series.