Summary of Large Language Model Guided Knowledge Distillation For Time Series Anomaly Detection, by Chen Liu et al.
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
by Chen Liu, Shibo He, Qihang Zhou, Shizhong Li, Wenchao Meng
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AnomalyLLM approach combines knowledge distillation with large language model (LLM) features for time series anomaly detection. This method leverages the strengths of LLM-based teacher networks and student networks to improve performance on a few available samples, addressing the limitations of self-supervised methods that require extensive training data. By incorporating prototypical signals into the student network and using synthetic anomalies, AnomalyLLM effectively consolidates normal feature extraction while increasing the representation gap between the two networks. This approach demonstrates state-of-the-art performance on 15 datasets, with an accuracy improvement of at least 14.5% in the UCR dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection in time series data is important for identifying unusual patterns that can indicate problems or abnormalities. A new method called AnomalyLLM uses a teacher-student network approach to find anomalies in time series data. The teacher network is trained on a large amount of data, while the student network is trained to mimic the teacher’s features. When the student network is tested, it looks for patterns that are different from what it learned from the teacher, which helps identify anomalies. To make this method more effective, the researchers added special signals and synthetic anomalies to help the student network learn normal patterns better. This approach outperformed other methods on 15 datasets. |
Keywords
* Artificial intelligence * Anomaly detection * Feature extraction * Knowledge distillation * Large language model * Self supervised * Time series
