SAGE decomposes univariate time-series anomaly detection into four specialized LLM analyzers plus an evidence-grounded detector and supervisor, achieving the highest average performance on three benchmarks while using only normal data for in-context examples.
Isolation forest
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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.
Benchmark study finds quantile and z-score marking strategies most robust for adaptive mesh refinement in steady mechanics problems, with Dörfler effective at large parameters and Isolation Forest competitive only under generous settings.
citing papers explorer
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Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
SAGE decomposes univariate time-series anomaly detection into four specialized LLM analyzers plus an evidence-grounded detector and supervisor, achieving the highest average performance on three benchmarks while using only normal data for in-context examples.
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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.
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Marking strategies for adaptive mesh refinement: An efficiency-focused benchmark study for steady solid and fluid mechanics problems
Benchmark study finds quantile and z-score marking strategies most robust for adaptive mesh refinement in steady mechanics problems, with Dörfler effective at large parameters and Isolation Forest competitive only under generous settings.