Summary of Large Language Models Can Be Zero-shot Anomaly Detectors For Time Series?, by Sarah Alnegheimish et al.
Large language models can be zero-shot anomaly detectors for time series?
by Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, Kalyan Veeramachaneni
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 have been shown to excel in a range of tasks, including time series forecasting. This paper presents a novel study on using these models for time series anomaly detection, which requires identifying anomalies within a sequence or multiple sequences. The authors introduce sigllm, a framework combining a time-series-to-text conversion module and end-to-end pipelines prompting language models to perform anomaly detection. They investigate two approaches: prompt-based detection, where the model is asked to identify anomalies directly, and forecasting-guided detection, leveraging the model’s forecasting capabilities to guide the anomaly detection process. The framework was evaluated on 11 datasets from various sources using 10 pipelines. Results show that the forecasting method outperformed the prompting method in all datasets with respect to the F1 score. While large language models are capable of finding anomalies, they still lag behind state-of-the-art deep learning models, achieving results 30% worse. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many cool things! This paper is about using them to find unusual patterns in time series data. Time series data is like a sequence of numbers that shows how something changes over time. For example, temperature readings from weather stations or stock prices. The problem with this kind of data is that it’s hard to tell what’s normal and what’s not. That’s where the large language models come in! They can be trained to find anomalies (unusual patterns) in the data. The authors created a new framework called sigllm that uses these models to detect anomalies. They tested their framework on 11 different datasets and found that it worked pretty well. However, they also found that other methods, like deep learning models, are still better at finding anomalies. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » F1 score » Prompt » Prompting » Temperature » Time series
