Exploring explicit and implicit graph learning for multivariate time series imputation

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.

Authors

• Yakun Chen
• Ruotong Hu
• Zihao Li
• Chao Yang School of Computer Science, Hubei University
• Xianzhi Wang
• Guodong Long Australian Artificial Intelligence Institute, University of Technology Sydney
• Guandong Xu University of Technology Sydney The Education University of Hong Kong

Keywords

• Graph neural network
• Multivariate time series imputation
• Spatio-temporal graph learning