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)

Paper ID: 6691
Venue: SIGMOD
Year: 2023
Pagerank: 5.4025905e-05
Overall Rank: 5,629 | 60.85%
DOI: 10.1145/3589333

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,900	Kamel: A Scalable BERT-based System for Trajectory Imputation	2024	VLDB	4.8925595e-05
9,242	ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data	2024	VLDB	4.3690661e-05
10,744	DIM-SUM: Dynamic IMputation for Smart Utility Management	2025	VLDB	4.1945683e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank

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