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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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 | Stars |
|---|---|
| 2026-07-24 | 512 |
| 2026-07-25 | 512 |
| 2026-07-28 | 512 |
| 2026-07-30 | 512 |
| 2026-07-31 | 513 |
| 2026-08-03 | 513 |
| 2026-08-06 | 513 |
Today
— stars today
This week
+1 stars this week
This month
— stars this month
Momentum
1.0
growth rate 0.19%/day
> [!TIP]
> **[Updates in May 2025]** 🎉 Our survey paper [Deep Learning for Multivariate Time Series Imputation: A Survey](https://arxiv.org/abs/2402.04059) gets accepted by IJCAI 2025!
We comprehensively review the literature of the state-of-the-art deep-learning imputation methods for time series, provide a new taxonomy based on uncertainty and model architecture for them, systematically compare multiple toolkits, and discuss the challenges and future directions in this field.
>
> **[Updates in Jun 2024]** 😎 The 1st comprehensive time-seres imputation benchmark paper
[TSI-Bench: Benchmarking Time Series Imputation](https://arxiv.org/abs/2406.12747) is now publicly available.
The code is open source in the repo [Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation).
With nearly 35,000 experiments, we provide a comprehensive benchmarking study on 28 imputation methods, 3 missing patterns (points, sequences, blocks),
various missing rates, and 8 real-world datasets.
>
> **[Updates in May 2024]** 🔥 We applied SAITS embedding and training strategies to **iTransformer, FiLM, FreTS, Crossformer, PatchTST, DLinear, ETSformer, FEDformer,
> Informer, Autoformer, Non-stationary Transformer, Pyraformer, Reformer, SCINet, RevIN, Koopa, MICN, TiDE, and StemGNN** in <a href="https://github.com/WenjieDu/PyPOTS"><img src="https://pypots.com/figs/pypots_logos/PyPOTS/logo_FFBG.svg" width="26px" align="center"/> PyPOTS</a>
> to enable them to be applicable to the time-series imputation task.
<p align="center">
<a id="SAITS" href="#SAITS">
<img src="https://pypots.com/figs/pypots_logos/SAITS/banner.jpg" alt="SAITS Title" title="SAITS Title" width="80%"/>
</a>
</p>
<p align="center">
<img src="https://img.shields.io/badge/Python-v3-E97040?logo=python&logoColor=white" />
<img alt="powered by PyTorch" src="https://img.shields.io/badge/PyTorch-❤️-F8C6B5?logo=pytorch&logoColor=white">
<a href="https://github.com/WenjieDu/SAITS/blob/main/LICENSE">
<img src="https://img.shields.io/badge/License-MIT-3C7699?logo=opensourceinitiative&logoColor=white" />
</a>
<a href="https://doi.org/10.1016/j.eswa.2023.119619">
<img src="https://img.shields.io/badge/ESWA-published-75C1C4?logo=elsevier&color=FF6C00" />
</a>
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=j9qvUg0AAAAJ&citation_for_view=j9qvUg0AAAAJ:Y0pCki6q_DkC" title="Paper citation number from Google Scholar">
<img src="https://pypots.com/figs/citation_badges/saits.svg" />
</a>
<a href="https://webofscience.clarivate.cn/wos/woscc/full-record/WOS:000943170100001?SID=USW2EC0D82x89d30RifxLVxJpho5Y" title="This is a Highly Cited Paper recognized by ESI">
<img src="https://pypots.com/figs/citation_badges/ESI_highly_cited_paper.svg" />
</a>
</p>
**‼️Kind reminder: This document can <ins>help you solve many common questions</ins>, please read it before you run the code.**
The official code repository is for the paper [SAITS: Self-Attention-based Imputation for Time Series](https://doi.org/10.1016/j.eswa.2023.119619)
(preprint on arXiv is [here](https://arxiv.org/abs/2202.08516)), which has been accepted by the journal
*[Expert Systems with Applications (ESWA)](https://www.sciencedirect.com/journal/expert-systems-with-applications)*
[2022 IF 8.665, CiteScore 12.2, JCR-Q1, CAS-Q1, CCF-C]. You may never have heard of ESWA,
while it was ranked 1st in Google Scholar under the top publications of Artificial Intelligence in 2016
([info source](https://www.sciencedirect.com/journal/expert-systems-with-applications/about/news#expert-systems-with-applications-is-currently-ranked-no1-in)), and is still the top 1 AI journal according to Google Scholar metrics
([here is the current ranking list](https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_artificialintelligence) FYI).
SAITS is the first work applying pure self-attention without any recursive design in the Excerpt of 15,185 characters
Read on GitHub
Wenjie Du · @TimeSeries-AI
95
3
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ffd215620706da9e, topic:deep-learning, topic:pytorch
matched fp:ffd215620706da9e, topic:transformer