Time-RA reformulates time series anomaly detection as a reasoning-intensive generative task and provides the RATs40K multimodal benchmark to evaluate and improve LLM-based diagnosis.
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Anomaly transformer: Time series anomaly detection with association discrepancy
10 Pith papers cite this work. Polarity classification is still indexing.
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TS-Reasoner is a domain-oriented agent using LLMs, computational tools, and error feedback for multi-step time series inference, showing better performance than general LLMs on understanding and reasoning benchmarks.
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.
DBR-AF decouples cross-variable correlations in reconstruction and applies autoregressive flows to model residual densities for improved anomaly detection in multivariate time series.
PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
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.
The paper presents architecture variants for observers and controllers in self-organizing cyber-physical energy systems that account for information and control constraints.
Fourier-KAN-Mamba combines Fourier features, KAN nonlinearities, and Mamba state-space modeling with a gating mechanism and reports better anomaly detection performance than prior methods on the MSL, SMAP, and SWaT benchmarks.
citing papers explorer
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Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
Time-RA reformulates time series anomaly detection as a reasoning-intensive generative task and provides the RATs40K multimodal benchmark to evaluate and improve LLM-based diagnosis.
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TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
TS-Reasoner is a domain-oriented agent using LLMs, computational tools, and error feedback for multi-step time series inference, showing better performance than general LLMs on understanding and reasoning benchmarks.
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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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Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation
DBR-AF decouples cross-variable correlations in reconstruction and applies autoregressive flows to model residual densities for improved anomaly detection in multivariate time series.
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Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning
PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
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Neural CDEs as Correctors for Learned Time Series Models
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
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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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Architectures for Robust Self-Organizing Energy Systems under Information and Control Constraints
The paper presents architecture variants for observers and controllers in self-organizing cyber-physical energy systems that account for information and control constraints.
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Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection
Fourier-KAN-Mamba combines Fourier features, KAN nonlinearities, and Mamba state-space modeling with a gating mechanism and reports better anomaly detection performance than prior methods on the MSL, SMAP, and SWaT benchmarks.
- Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection