Summary of Position: What Can Large Language Models Tell Us About Time Series Analysis, by Ming Jin et al.
Position: What Can Large Language Models Tell Us about Time Series Analysis
by Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 paper argues that large language models (LLMs) have the potential to revolutionize time series analysis by enabling efficient decision-making and advancing towards a more universal form of time series analytical intelligence. Current LLMs can be used to analyze time series data, which could unlock possibilities such as time series modality switching and question answering. The authors encourage researchers and practitioners to recognize the potential of LLMs in advancing time series analysis and emphasize the need for trust in these related efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are really smart computers that can understand and analyze lots of information, including things like how numbers change over time. Right now, these models are mostly good at predicting what will happen next, but they could be used to do all sorts of other cool things with time series data, like switching between different types of data or answering questions about it. |
Keywords
* Artificial intelligence * Question answering * Time series
