UFGTime: Mining Intertwined Dependencies in Multivariate Time Series via an Efficient Pure Graph Approach
Summary: UFGTime: pure-graph forecasting encoding intertwined inter/intra-series dependencies as a compact spectral-variate graph from frequency similarities. Graph-framelet message passing avoids over-smoothing and reduces cost from O((NT)^2) to near-linear O(kNT), enabling scalable multivariate forecasting. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ruikun Li
- 2. Dai Shi
- 3. Ye Xiao
- 4. Junbin Gao
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| Rank | Cited Paper | Year | Venue | Pagerank |
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| 3,318 | TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods | 2024 | VLDB | 7.5863696e-05 |
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