GinAR plus : A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values

doi:10.1109/TKDE.2025.3569649

	GinAR plus : A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values
	Yu, Chengqing 1,2; Wang, Fei 1,2; Shao, Zezhi 3; Qian, Tangwen 3; Zhang, Zhao 3; Wei, Wei 4; An, Zhulin 3; Wang, Qi 3; Xu, Yongjun 1,2
	2025-08-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	37 期号:8 页码:4635-4648
摘要	Spatial-Temporal Graph Neural Networks (STGNNs) have been widely utilized in multivariate time series forecasting (MTSF), but they rely on the assumption of data completeness. In practice, due to factors such as natural disaster, STGNNs frequently encounter the challenge of missing data resulting from numerous malfunctioning data collectors. In this case, on the one hand, due to the presence of missing values, STGNNs easily generate incorrect spatial correlations, leading to the performance degradation. On the other hand, STGNNs require separate training of models for different missing rates, limiting their robustness. To address these challenges, we first propose two important components (interpolation attention and adaptive graph convolution), which utilize normal values to recover missing values into reliable representations and reconstruct spatial correlations. Then, we replace the fully connected layers in simple recursive units with these two components and propose Graph Interpolation Attention Recursive Network (GinAR), aiming to recursively correct spatial correlations and achieve end-to-end MTSF with missing values. Finally, we use data with different missing rates as positive and negative data pairs. By employing contrastive learning to train GinAR, we propose GinAR+ and enhance its robustness to data with different missing rates. Experiments validate the superiority of GinAR+ and our motivation.
关键词	Correlation Predictive models Forecasting Time series analysis Data models Robustness Adaptation models Imputation Contrastive learning Training graph interpolation attention recursive network multivariate time series forecasting with missing values
DOI	10.1109/TKDE.2025.3569649
收录类别	SCI
语种	英语
资助项目	NSFC[62372430] ; CPSF[GZC20241758] ; Youth Innovation Promotion Association CAS[2023112]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001525525600027
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42043
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Fei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China
推荐引用方式 GB/T 7714	Yu, Chengqing,Wang, Fei,Shao, Zezhi,et al. GinAR plus : A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2025,37(8):4635-4648.
APA	Yu, Chengqing.,Wang, Fei.,Shao, Zezhi.,Qian, Tangwen.,Zhang, Zhao.,...&Xu, Yongjun.(2025).GinAR plus : A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,37(8),4635-4648.
MLA	Yu, Chengqing,et al."GinAR plus : A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 37.8(2025):4635-4648.