Multiple Time Series Forecasting with Dynamic Graph Modeling
Summary: Introduces MTSF-DG which learns historical relation graphs and predicts future relation graphs to model evolving inter-series correlations. Employs a causal GNN and an explicit reasoning network to learn time-varying influence for improved multivariate forecasting. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Kai Zhao (Aalborg University)
- 2. Chenjuan Guo (East China Normal University)
- 3. Yunyao Cheng (Aalborg University)
- 4. Peng Han (University of Electronic Science and Technology of China)
- 5. Miao Zhang (Aalborg University; Harbin Engineering University)
- 6. Bin Yang (East China Normal University)
BibTeX Citation
@article{zhao_vldb24,
title = {{Multiple Time Series Forecasting with Dynamic Graph Modeling}},
author = {Zhao, Kai and Guo, Chenjuan and Cheng, Yunyao and Han, Peng and Zhang, Miao and Yang, Bin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {4},
pages = {753--765},
doi = {10.14778/3636218.3636230},
url = {https://doi.org/10.14778/3636218.3636230},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,694 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 7.1937882e-05 |
| 4,851 | Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles | 2022 | VLDB | 6.4803317e-05 |
| 5,129 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 6.3548172e-05 |
| 6,443 | AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting | 2023 | SIGMOD | 5.8775356e-05 |
| 11,350 | Weakly Guided Adaptation for Robust Time Series Forecasting | 2024 | VLDB | 5.093636e-05 |
| 11,402 | LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation | 2023 | SIGMOD | 5.093636e-05 |
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