Summary of Revisited Large Language Model For Time Series Analysis Through Modality Alignment, by Liangwei Nathan Zheng et al.
Revisited Large Language Model for Time Series Analysis through Modality Alignment
by Liangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
First submitted to arxiv on: 16 Oct 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 The paper investigates the effectiveness of Large Language Models (LLMs) for key time series tasks such as forecasting, classification, imputation, and anomaly detection. It compares LLMs’ performance against simpler baseline models like linear regressions and randomly initialized LLMs. The results show that LLMs offer minimal advantages and may even distort the temporal structure of data, while simpler models consistently outperform them. Additionally, the paper analyzes existing reprogramming techniques and shows that they fail to effectively align time series data with language, displaying pseudo-alignment behavior in embedding space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very smart computers that can do lots of things well. But when it comes to predicting what will happen next based on past data (called “time series tasks”), these models aren’t much better than simpler ones. In fact, they might even make the predictions worse! This paper looks at how LLMs perform compared to simpler models and finds that they don’t do much better and can actually mess up the timing of the events. |
Keywords
» Artificial intelligence » Alignment » Anomaly detection » Classification » Embedding space » Time series
