Summary of Timerag: Boosting Llm Time Series Forecasting Via Retrieval-augmented Generation, by Silin Yang et al.
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
by Silin Yang, Dong Wang, Haoqi Zheng, Ruochun Jin
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed TimeRAG framework combines Retrieval-Augmented Generation (RAG) with large language models (LLMs) for time series forecasting, constructing a knowledge base from historical sequences and retrieving similar patterns using Dynamic Time Warping (DTW). The RAG component improves the prediction accuracy of LLMs by 2.97% on average across various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TimeRAG is a new way to make predictions about what will happen next in a series of numbers over time. It uses something called large language models, which are really good at understanding language and making predictions. But they need help remembering what happened before, so TimeRAG creates a kind of library of previous sequences that it can look at when trying to predict the future. This makes its predictions more accurate. |
Keywords
» Artificial intelligence » Knowledge base » Rag » Retrieval augmented generation » Time series
