PhaseNet++ detects anomalies in industrial control systems by processing both magnitude and phase from STFT using a Phase Coherence Index graph and dual-head decoder, achieving 90.98% F1 on the SWaT benchmark.
Graph neural network-based anomaly detection in multivariate time series
3 Pith papers cite this work. Polarity classification is still indexing.
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LLM-derived measurement semantics enable a lightweight pre-inference correction step that reduces industrial prediction MAE by 30.7% on real tests and 80.3% under controlled sensor corruption.
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.
citing papers explorer
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PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs
PhaseNet++ detects anomalies in industrial control systems by processing both magnitude and phase from STFT using a Phase Coherence Index graph and dual-head decoder, achieving 90.98% F1 on the SWaT benchmark.
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LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
LLM-derived measurement semantics enable a lightweight pre-inference correction step that reduces industrial prediction MAE by 30.7% on real tests and 80.3% under controlled sensor corruption.
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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.