Summary of Large Language Models For Time Series: a Survey, by Xiyuan Zhang et al.
Large Language Models for Time Series: A Survey
by Xiyuan Zhang, Ranak Roy Chowdhury, Rajesh K. Gupta, Jingbo Shang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper surveys the application of Large Language Models (LLMs) to time series analysis, a domain that encompasses climate, IoT, healthcare, traffic, audio, and finance. While LLMs were originally trained on text data, this study explores strategies for leveraging their capabilities in numerical time series analysis. The authors detail various methodologies, including direct prompting, time series quantization, aligning techniques, utilization of the vision modality as a bridging mechanism, and combinations with other tools. Additionally, the paper provides an overview of existing multimodal time series and text datasets, highlighting challenges and future opportunities in this emerging field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how we can use powerful language models to analyze data that changes over time, like weather patterns or traffic flows. Right now, these models are great for understanding written words, but they struggle with numbers and patterns. The authors of this paper explore ways to fix this problem by using the language models in new ways. They also review existing datasets that combine text and numbers, showing how we can use these combinations to make better predictions and decisions. |
Keywords
* Artificial intelligence * Prompting * Quantization * Time series
