POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.
Practical approach to asynchronous multivariate time series anomaly detection and localization
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Standard OLS fairness tests for deterministic pricing algorithms use invalid standard errors; corrected estimators reveal that all 34 tested Illinois auto insurers discriminate against minority zip codes.
Introduces a cyclic-dynamics dataset for industrial MTSAD and benchmarks federated anomaly detection methods on it and a public dataset.
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
citing papers explorer
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POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection
POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.