SAITS is a PyTorch implementation of a deep learning model for time-series imputation, using self-attention mechanisms to reconstruct incomplete multivariate time series containing NaN values. The model applies dual self-attention blocks to jointly capture temporal dependencies and feature correlations for imputation tasks. The repository includes training utilities, benchmark scripts, and integrations with other time-series models via the PyPOTS framework.