Summary of See It, Think It, Sorted: Large Multimodal Models Are Few-shot Time Series Anomaly Analyzers, by Jiaxin Zhuang et al.
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers
by Jiaxin Zhuang, Leon Yan, Zhenwei Zhang, Ruiqi Wang, Jiawei Zhang, Yuantao Gu
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a pioneering framework called Time Series Anomaly Multimodal Analyzer (TAMA) that leverages Large Multimodal Models (LMMs) to enhance both the detection and interpretation of anomalies in time series data. TAMA reduces dependence on extensive labeled datasets by converting time series into visual formats that LMMs can efficiently process, leveraging few-shot in-context learning capabilities. The methodology is validated through rigorous experimentation on multiple real-world datasets, outperforming state-of-the-art methods in TSAD tasks while providing rich, natural language-based semantic analysis for deeper insights. Additionally, the paper contributes one of the first open-source datasets that includes anomaly detection labels, anomaly type labels, and contextual description, facilitating exploration and advancement within this critical field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to find weird patterns in time series data, like when a website is having trouble or a computer system fails. Usually, people need to do lots of work to prepare the data before they can use special tools to find these problems. But this new method, called TAMA, uses powerful computer models that can learn from very little information and also explain why something is weird. The researchers tested TAMA on many real-world datasets and it worked better than other methods. They also shared a big dataset with lots of examples so others can try to use TAMA too. |
Keywords
» Artificial intelligence » Anomaly detection » Few shot » Time series
