Informer2020
The GitHub repository for the paper "Informer" accepted by AAAI 2021. (by zhouhaoyi)
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 (by WenjieDu)
Our great sponsors
| Informer2020 | SAITS | |
|---|---|---|
| 2 | 9 | |
| 4,915 | 252 | |
| - | - | |
| 0.6 | 6.7 | |
| about 2 months ago | 20 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Informer2020
Posts with mentions or reviews of Informer2020.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-24.
-
[R] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Code for https://arxiv.org/abs/2012.07436 found: https://github.com/zhouhaoyi/Informer2020
- [R][D] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Zhou et al. AAAI21 Best Paper. ProbSparse self-attention reduces complexity to O(nlogn), generative style decoder to obtainsequence output in one step, and self-attention distilling for further reducing memory
SAITS
Posts with mentions or reviews of SAITS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-29.
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SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023.
The full paper is available on arXiv: https://arxiv.org/abs/2202.08516. The code on GitHub: https://github.com/WenjieDu/SAITS/. If your research lies in time-series modeling, you may also be interested in the work PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series. Its full paper is available on arXiv as well https://arxiv.org/abs/2305.18811, which has been peer-reviewed and accepted by the 9th SIGKDD international workshop Mining and Learning from Time Series (MiLeTS'23).
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[R] SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023.
Missing values in time series collected from the real world are common to see and very pesky. A new state-of-the-art and fast neural network called "SAITS“ is proposed to help impute missing data in partially-observed multivariate time series. The paper has been peer-reviewed and published in the journal Expert Systems with Applications (DOI link). The full paper is available on arXiv at this URL. The code is open source on GitHub https://github.com/WenjieDu/SAITS/.
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Missing values in time series collected from the real world are common to see and very pesky. A new state-of-the-art and fast neural network called SAITS is proposed to impute missing data in partially-observed multivariate time series. The code is open source on GitHub.
The code on GitHub: https://github.com/WenjieDu/SAITS/.
- SAITS: NEW Data - star count:118.0
What are some alternatives?
When comparing Informer2020 and SAITS you can also consider the following projects:
pytorch-forecasting - Time series forecasting with PyTorch
neural_prophet - NeuralProphet: A simple forecasting package
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).