SAITS: Self-attention-based imputation for time series

Wenjie Du, David Côté, Yan Liu

Abstract: Highlights•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.