Exploring explicit and implicit graph learning for multivariate time series imputation

Yakun CHEN, Ruotong HU, Zihao LI, Chao YANG, Xianzhi WANG, Guodong LONG, Guandong XU

Research output: Contribution to journalArticlespeer-review


Multivariate time series inherently contain missing values due to various issues, including incorrect data entry, broken equipment, and package loss during data transferring. The successful completion of time series data analysis tasks heavily relies on the essential task of imputing missing values. Inter-variable relationships in time series are typically overlooked by missing value imputation techniques. Although some graph-based algorithms can capture these relationships, the design of graph structures is commonly handcrafted and dataset-centric. We introduce a novel Explicit and Implicit Graph Recurrent Network (EIGRN) for multivariate time series imputation that integrates graph and recurrent neural networks to capture variable and time dependencies together. This proficiency is achieved by effectively integrating external data sources such as domain knowledge and the implicit relationships among nodes. In order to make our approach more applicable to datasets with larger numbers of missing values, we additionally discuss the model's performance for various missing value ratios. Our comprehensive experiments on real-world datasets show that our model outperforms state-of-the-art baselines in different industrial fields. Copyright © 2023 Elsevier Ltd. All rights reserved.

Original languageEnglish
Article number107217
JournalEngineering Applications of Artificial Intelligence
Issue numberPart A
Early online dateOct 2023
Publication statusPublished - Jan 2024


Chen, Y., Hu, R., Li, Z., Yang, C., Wang, X., Long, G., & Xu, G. (2024). Exploring explicit and implicit graph learning for multivariate time series imputation. Engineering Applications of Artificial Intelligence, 127(Part A), Article 107217. https://doi.org/10.1016/j.engappai.2023.107217


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