SAITS: Self-Attention-based Imputation for Time Series

SAITS is a novel method based on the self-attention mechanism for missing value imputation in multivariate time series that outperforms the state-of-the-art methods on the time-series imputation task efficiently and reveals its potential to improve the learning performance of pattern recognition models on incomplete time- series data from the real world.