Summary of The Rise Of Diffusion Models in Time-series Forecasting, by Caspar Meijer and Lydia Y. Chen
The Rise of Diffusion Models in Time-Series Forecasting
by Caspar Meijer, Lydia Y. Chen
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 survey explores the application of diffusion models in time-series forecasting, leveraging state-of-the-art results from various generative AI fields. The paper provides comprehensive background information on diffusion models, detailing conditioning methods and reviewing their use in time-series forecasting. It analyzes 11 specific implementations, discussing intuition, theory, effectiveness on different datasets, and comparative performance among each other. The key contributions include a thorough exploration of diffusion model applications in time-series forecasting and a chronological overview of these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey uses diffusion models to forecast future events in a series of numbers over time. It looks at the best ways to use these models and how they work, then compares them on different datasets. The results are really good, and it shows what’s possible with this type of AI. This is important for people who study AI or analyze data. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Time series
