Summary of In-context Fine-tuning For Time-series Foundation Models, by Abhimanyu Das et al.
In-Context Fine-Tuning for Time-Series Foundation Models
by Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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 This paper presents a novel methodology for fine-tuning time-series foundation models for zero-shot forecasting. The proposed approach involves prompting the model with multiple related time-series examples at inference time to forecast a target time-series into the future. The model is trained to utilize context windows from multiple related time-series, in addition to its own history, to adapt to the target domain’s distribution. Experimental results show that this approach outperforms supervised deep learning methods, statistical models, and other time-series foundation models on popular forecasting benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to improve forecasts using big data. It shows how to take a special kind of AI model called a time-series foundation model and make it better at predicting what will happen next by giving it information from similar things that have happened before. This helps the model learn more quickly and accurately, which is important for things like predicting weather or stock prices. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Inference » Prompting » Supervised » Time series » Zero shot
