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 (2 years ago)

Commits 84 (last one 2 months ago)

Stargazers 330 (2 this week)

Watchers 6 (0 this week)

Forks 51

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RepositoryStats indexes 584,777 repositories, of these WenjieDu/SAITS is ranked #123,773 (79th percentile) for total stargazers, and #297,959 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #21,073/116,437.

WenjieDu/SAITS is also tagged with popular topics, for these it's ranked: deep-learning (#2,659/8398), machine-learning (#2,558/7935), pytorch (#1,811/5951), transformer (#290/1000), time-series (#204/659)

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WenjieDu/SAITS has Github issues enabled, there is 1 open issue and 27 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: 2024-11-21 @ 04:12am, id: 435921975 / R_kgDOGfukNw