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https://github.com/WenjieDu/PyPOTS
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation, classification, clustering, forecasting, & anomaly detection on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
https://github.com/WenjieDu/PyPOTS
classification clustering data-mining data-science deep-learning forecasting healthcare imputation incomplete industrial interpolation machine-learning missing-values missingness neural-network partially-observed-time-series pytorch science-research time-series time-series-analysis
Last synced: about 1 month ago
JSON representation
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation, classification, clustering, forecasting, & anomaly detection on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
- Host: GitHub
- URL: https://github.com/WenjieDu/PyPOTS
- Owner: WenjieDu
- License: bsd-3-clause
- Created: 2022-03-29T14:22:47.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-28T16:11:34.000Z (about 1 month ago)
- Last Synced: 2024-07-29T10:34:19.576Z (about 1 month ago)
- Topics: classification, clustering, data-mining, data-science, deep-learning, forecasting, healthcare, imputation, incomplete, industrial, interpolation, machine-learning, missing-values, missingness, neural-network, partially-observed-time-series, pytorch, science-research, time-series, time-series-analysis
- Language: Python
- Homepage: https://pypots.com
- Size: 2.63 MB
- Stars: 868
- Watchers: 13
- Forks: 87
- Open Issues: 19
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Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-time-series - PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
- awesome-time-series - PyPOTS - observed time series (A.K.A. irregularly-sampled time series), supporting tasks of forecasting/imputation/classification/clustering on incomplete multivariate time series with missing values. (📦 Packages / Python)