METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting
Summary: METRO GNN framework for multivariate time series using multiscale temporal graphs to model dynamic cross-scale correlations. Cross-scale sampling/fusion enables efficient propagation; preserves intra/interstep relations and unifies GNN-TS models. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yue Cui
- 2. Kai Zheng
- 3. Dingshan Cui
- 4. Jiandong Xie
- 5. Liwei Deng
- 6. Feiteng Huang
- 7. Xiaofang Zhou
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,601 | BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks | 2024 | VLDB | 6.6811778e-05 |
| 6,168 | EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs | 2023 | SIGMOD | 6.028641e-05 |
| 8,377 | Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense | 2024 | VLDB | 5.4970753e-05 |
| 8,782 | A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis | 2024 | VLDB | 5.4311509e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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