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SAITS Alternatives
Similar projects and alternatives to SAITS based on common topics and language
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PyPOTS
A Python toolbox/library for reality-centric machine/deep learning and data mining on partially-observed time series with PyTorch, including SOTA neural network models for science tasks of imputation, classification, clustering, forecasting & anomaly detection on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better SAITS alternative or higher similarity.
SAITS reviews and mentions
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
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Stats
Basic SAITS repo stats
9
256
6.6
10 days ago
WenjieDu/SAITS is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of SAITS is Python.
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