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 (University of Sydney)
- 2. Dai Shi (University of Sydney)
- 3. Ye Xiao (University of Sydney)
- 4. Junbin Gao (University of Sydney)
BibTeX Citation
@article{li_vldb25,
title = {{UFGTime: Mining Intertwined Dependencies in Multivariate Time Series via an Efficient Pure Graph Approach}},
author = {Li, Ruikun and Shi, Dai and Xiao, Ye and Gao, Junbin},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {9},
pages = {3175--3188},
doi = {10.14778/3746405.3746436},
url = {https://doi.org/10.14778/3746405.3746436},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,389 | TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods | 2024 | VLDB | 7.4394209e-05 |
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