Summary of Sagdfn: a Scalable Adaptive Graph Diffusion Forecasting Network For Multivariate Time Series Forecasting, by Yue Jiang et al.
SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting
by Yue Jiang, Xiucheng Li, Yile Chen, Shuai Liu, Weilong Kong, Antonis F. Lentzakis, Gao Cong
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A new approach to scalable spatial-temporal graph neural networks is introduced, aiming to improve multivariate time series forecasting performance. The proposed Scalable Adaptive Graph Diffusion Forecasting Network (SAGDFN) can capture complex spatial and temporal dependencies in large-scale datasets without prior knowledge of spatial correlation. Compared to existing methods, SAGDFN achieves state-of-the-art performance on three real-world datasets with thousands of nodes, while demonstrating comparable results on a dataset with 207 nodes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for our daily lives. This method helps predict patterns in multiple time series data by using spatial-temporal graph neural networks (STGNNs). However, current methods are limited because they can only handle small amounts of data. A new approach called SAGDFN solves this problem by being able to handle large datasets without needing prior knowledge about the relationships between different sensors. This leads to better predictions and a more scalable method. |
Keywords
» Artificial intelligence » Diffusion » Time series
