MacrOData supplies three large, curated benchmark suites totaling 2,446 datasets for tabular outlier detection, complete with standardized splits, metadata, and a public leaderboard.
Breunig, Hans-Peter Kriegel, Raymond T
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
citing papers explorer
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MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection
MacrOData supplies three large, curated benchmark suites totaling 2,446 datasets for tabular outlier detection, complete with standardized splits, metadata, and a public leaderboard.
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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.
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On Diffusion Modeling for Anomaly Detection
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.