WenjieDu / SAITS

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

Date Created 2021-12-07 (3 years ago)

Commits 84 (last one 6 months ago)

Stargazers 372 (2 this week)

Watchers 6 (0 this week)

Forks 56

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RepositoryStats indexes 621,960 repositories, of these WenjieDu/SAITS is ranked #117,381 (81st percentile) for total stargazers, and #304,529 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #20,143/126,101.

WenjieDu/SAITS is also tagged with popular topics, for these it's ranked: deep-learning (#2,536/8739), machine-learning (#2,466/8306), pytorch (#1,718/6164), transformer (#275/1058), time-series (#193/692)

Other Information

WenjieDu/SAITS has Github issues enabled, there are 2 open issues and 29 closed issues.

Homepage URL: https://doi.org/10.1016/j.eswa.2023.119619

All Topics

impute pytorch attention imputation time-series transformer deep-learning interpolation missing-values self-attention incomplete-data imputation-model machine-learning irregular-sampling partially-observed attention-mechanism incomplete-time-series time-series-imputation partially-observed-data partially-observed-time-series

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83 commits on the default branch (main) since jan '22

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updated: 2025-03-01 @ 12:49am, id: 435921975 / R_kgDOGfukNw