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.
arXiv preprint arXiv:2002.09545 (2020)
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Cascade-KDE is a training-free framework for restoring corrupted time series that estimates a temporal-amplitude density, applies density-truncated robust expectation, and refines via exponential cascade to preserve local structure and derivatives.
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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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Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions
Cascade-KDE is a training-free framework for restoring corrupted time series that estimates a temporal-amplitude density, applies density-truncated robust expectation, and refines via exponential cascade to preserve local structure and derivatives.