SAITS: Self-attention-based imputation for time series

• Published in: Expert systems with applications Vol. 219; p. 119619
• Main Authors: Du, Wenjie, Côté, David, Liu, Yan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2023
• Subjects: Imputation model; Missing values; Neural network; Self-attention; Time series; Neural network; Missing values; Imputation model; Time series; Self-attention
• DOI: 10.1016/j.eswa.2023.119619
• ISSN: 0957-4174

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. •Address the missing data problem in time-series analysis tasks by deep learning.•A novel self-attention model imputes missing values in incomplete time series.•Our method solves the disadvantages of previous RNN-based imputation models.•SAITS has a better imputation model architecture than Transformer.•SAITS achieves the state-of-the-art performance on the time-series imputation task.