Summary of Mg-tsd: Multi-granularity Time Series Diffusion Models with Guided Learning Process, by Xinyao Fan et al.
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
by Xinyao Fan, Yueying Wu, Chang Xu, Yuhao Huang, Weiqing Liu, Jiang Bian
First submitted to arxiv on: 9 Mar 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 proposed Multi-Granularity Time Series Diffusion (MG-TSD) model leverages the inherent granularity levels within data to guide the learning process of diffusion models, achieving state-of-the-art predictive performance in probabilistic time series forecasting. By introducing a novel multi-granularity guidance diffusion loss function and concise implementation method, MG-TSD effectively utilizes coarse-grained data across various granularity levels without relying on additional external data. The model outperforms existing time series prediction methods in extensive experiments conducted on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The MG-TSD model is a new way to use diffusion probabilistic models for forecasting future events in time series data, like stock prices or weather patterns. This approach works by gradually losing small details in the data and using that information to help the model learn. The result is a better prediction of what might happen next. This method doesn’t need extra data, making it useful for many different types of problems. |
Keywords
* Artificial intelligence * Diffusion * Loss function * Time series
