Summary of Ginar: An End-to-end Multivariate Time Series Forecasting Model Suitable For Variable Missing, by Chengqing Yu et al.
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
by Chengqing Yu, Fei Wang, Zezhi Shao, Tangwen Qian, Zhao Zhang, Wei Wei, Yongjun Xu
First submitted to arxiv on: 18 May 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 This paper addresses a critical challenge in multivariate time series forecasting (MTSF), where traditional Spatial-Temporal Graph Neural Networks (STGNNs) struggle to model spatial-temporal dependencies when faced with incomplete or missing data. The authors propose a novel Graph Interpolation Attention Recursive Network (GinAR) that can recover missing variables and reconstruct correct spatial-temporal dependencies, enabling accurate forecasting even when 90% of variables are missing. GinAR consists of two key components: interpolation attention for recovering missing variables and adaptive graph convolution to model spatial-temporal dependencies. The authors demonstrate the effectiveness of GinAR by comparing it with 11 state-of-the-art baselines on five real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GinAR is a new way to forecast things that happen together over time. Right now, computers are bad at doing this when some information is missing. They try to use what they have, but it doesn’t work well. The authors of this paper wanted to fix this problem. They created GinAR, which can fill in the gaps and make good predictions even if a lot of information is missing. This is important because sometimes data isn’t collected correctly or takes a long time to fix. The authors tested GinAR on real-world examples and showed that it works better than other ways of doing things. |
Keywords
» Artificial intelligence » Attention » Time series
