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Found 2 papers, 1 papers with code
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
To overcome the limitation, we propose \textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets.
TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision
Then, the subsequence features are extracted to determine the presence of collective anomalies.
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