Summary of Can Llms Serve As Time Series Anomaly Detectors?, by Manqing Dong et al.
Can LLMs Serve As Time Series Anomaly Detectors?
by Manqing Dong, Hao Huang, Longbing Cao
First submitted to arxiv on: 6 Aug 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 Large language models (LLMs) have been applied to time series forecasting with promising results, but a crucial aspect remains underexplored: detecting and explaining time series anomalies. This paper investigates the capabilities of GPT-4 and LLaMA3 in this task, revealing that while LLMs cannot be directly used for anomaly detection, designed prompt strategies can enable GPT-4 to detect anomalies with competitive results. A synthesized dataset is proposed to generate time series anomalies with explanations, allowing LLaMA3 to demonstrate improved performance in anomaly detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict what will happen next in a series of numbers that show patterns and trends over time. This paper looks at how large language models can help detect when something unusual happens in these time series – like a sudden change or an unexpected pattern. The results show that these models are promising for this task, especially if we design the right prompts to get them to focus on finding anomalies. |
Keywords
» Artificial intelligence » Anomaly detection » Gpt » Prompt » Time series
