DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation
Summary: Dynamic adjacency matrices capture evolving spatial correlations in multivariate time series, decomposing into constant, long-term trends and periodic patterns. DAMR aggregates these dynamic graphs and applies a graph representation learning layer for missing-value imputation, yielding up to 19.4% MAE improvement over SOTA. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaobin Ren (University of Auckland)
- 2. Kaiqi Zhao (University of Auckland)
- 3. Patricia Riddle (University of Auckland)
- 4. Katerina Taškova (University of Auckland)
- 5. Lianyan Li (University of Auckland)
- 6. Qingyi Pan (Tsinghua University)
BibTeX Citation
@inproceedings{ren_sigmod23,
title = {{DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation}},
author = {Ren, Xiaobin and Zhao, Kaiqi and Riddle, Patricia and Taškova, Katerina and Li, Lianyan and Pan, Qingyi},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589333},
url = {https://dl.acm.org/doi/10.1145/3589333},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,651 | Kamel: A Scalable BERT-based System for Trajectory Imputation | 2024 | VLDB | 5.4772833e-05 |
| 9,573 | ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data | 2024 | VLDB | 5.1571823e-05 |
| 11,363 | DIM-SUM: Dynamic Imputation for Smart Utility Management | 2025 | VLDB | 4.9793485e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|
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