Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis. A popular solution is imputation, where the fundamental challenge is to determine what values should be filled in. This paper proposes SAITS, a novel method based on the self-attention mechanism for missing value imputation in multivariate time series. Trained by a joint-optimization approach, SAITS learns missing values from a weighted combination of two diagonally-masked self-attention (DMSA) blocks. DMSA explicitly captures both the temporal dependencies and feature correlations between time steps, which improves imputation accuracy and training speed. Meanwhile, the weighted-combination design enables SAITS to dynamically assign weights to the learned representations from two DMSA blocks according to the attention map and the missingness information. Extensive experiments quantitatively and qualitatively demonstrate that SAITS outperforms the state-of-the-art methods on the time-series imputation task efficiently and reveal SAITS' potential to improve the learning performance of pattern recognition models on incomplete time-series data from the real world. The code is open source on GitHub at https://github.com/WenjieDu/SAITS.
Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism
School of Computer Science, Guangdong University of Technology（广东技术大学计算机科学学院）；Peng Cheng Laboratory（鹏城实验室）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；College of Science, Shantou University（汕头大学理学院）
Missing data imputation for noisy time-series data and applications in healthcare
Faculty of Mathematics and Computer Science, University of Science（胡志明市科学大学数学与计算机科学学院）；Vietnam National University（越南国家大学）；University of Medicine and Pharmacy at Ho Chi Minh City（胡志明市医药大学）；Simula Metropolitan（西穆拉都会研究院）；AISIA Research Laboratory（AISIA研究院）
Exploiting the Prior of Generative Time Series Imputation
Imperial College London, Department of Electronic and Electrical Engineering（帝国理工学院伦敦校区电子与电气工程系）；University of Science and Technology of China（中国科学技术大学）；Tsinghua University, Department of CST（清华大学计算机科学与技术系）；Shengshu AI（盛数人工智能）
Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset
University of Macau（澳门大学）；Northeast Normal University（东北师范大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Oxford（牛津大学）；Dalian Maritime University（大连海事大学）
Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective
State Key Lab of General AI, School of Intelligence Science and Technology, Peking University（人工智能国家重点实验室、智能科学与技术学院、北京大学）；Zhejiang University（浙江大学）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Center for Data Science, Peking University（北京大学数据科学中心）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Pazhou Laboratory (Huangpu), Guangzhou, China（黄埔实验室（华南））
PyPOTS Research（PyPOTS研究机构）；HKUST（香港科技大学）；King’s College London（伦敦国王学院）；University of Oxford（牛津大学）；Sichuan University（四川大学）；University of Washington（华盛顿大学）；GaTech（佐治亚理工学院）；HKUST(GZ)（香港科技大学（广州））；Northwest University（西北大学）；Squirrel AI（松鼠AI）
ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University（浙江可感知与智能系统重点实验室，计算机科学学院，浙江大学）；The Zhejiang University-University of Illinois Urbana-Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）
Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
Central South University（中南大学）；Aalborg University（奥尔堡大学）；The Hong Kong Polytechnic University（香港理工大学）；National University of Defense Technology（国防科技大学）
Beyond Random Missingness: Clinically Rethinking for Healthcare Time Series Imputation
King’s College London（伦敦国王学院）；PyPOTS Research（PyPOTS研究中心）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology（香港科技大学）；University College London（伦敦大学学院）
Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series
Department of Computer and Information（计算机与信息系）；Hefei University of Technology（合肥工业大学）；Dept. of Comp. Sci. & Tech.（计算机科学与技术系）；Tsinghua University（清华大学）
Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification
1 Department of Statistics, Brigham Young University, Provo, UT, USA Emails；2 Department of Computer Science, Brigham Young University, Provo, UT, USA Email；3 Department of Public Health, Brigham Young University, Provo, UT, USA Email；4 Department of Neurology, Vagelos College of Physicians \& Surgeons, Columbia University, New York, NY, USA Email；5 Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA Email；6 Elmhurst Hospital Center, NYC Health + Hospitals, New York, NY, USA Email；7 Department of Mathematics \& Statistics, Utah State University, Logan, UT, USA Email
