Summary of Empowering Time Series Analysis with Large Language Models: a Survey, by Yushan Jiang et al.
Empowering Time Series Analysis with Large Language Models: A Survey
by Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
First submitted to arxiv on: 5 Feb 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 This paper surveys recent advances in using large language models (LLMs) for time series analysis. Despite their impressive capabilities in natural language tasks, training a general-purpose model from scratch is challenging for time series data due to its sheer volume, variety, and non-stationarity. The authors summarize existing methods that leverage LLMs, categorizing them into direct query, tokenization, prompt design, fine-tune, and model integration. They also discuss applications in various domains, including general and spatial-temporal time series data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores the use of large language models (LLMs) to analyze time series data. LLMs are great at understanding natural language, but it’s hard to train one from scratch for time series because there’s so much data and it keeps changing. The authors look at different ways to use LLMs, like asking questions directly or designing special prompts. They also talk about how these models can be used in different areas, such as finance or weather forecasting. |
Keywords
* Artificial intelligence * Prompt * Time series * Tokenization
