Summary of Retrieval Augmented Time Series Forecasting, by Kutay Tire et al.
Retrieval Augmented Time Series Forecasting
by Kutay Tire, Ege Onur Taga, Muhammed Emrullah Ildiz, Samet Oymak
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent paper proposes a novel framework called Retrieval Augmented Forecasting (RAF) to improve zero-shot forecasting performance in time-series data. The authors build upon retrieval-augmented generation (RAG), a key component of modern language models, and adapt it for use with time-series foundation models (TSFMs). Specifically, they develop efficient strategies for retrieving related time-series examples and incorporating them into forecasts. Experimental results demonstrate that RAF improves forecasting accuracy across diverse domains, with the improvement more pronounced for larger TSFM sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is important because it shows how a technique called retrieval-augmented generation can be used to make predictions about what will happen in the future (forecasting) using time-series data. The authors created a new way of doing this called Retrieval Augmented Forecasting, or RAF, and tested it on different types of data. They found that using RAF made their predictions more accurate. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation » Time series » Zero shot
