GitHub / 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/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WenjieDu%2FPyPOTS
Stars: 1,358
Forks: 131
Open issues: 44
License: bsd-3-clause
Language: Python
Size: 3.98 MB
Dependencies parsed at: Pending
Created at: about 3 years ago
Updated at: 8 days ago
Pushed at: 10 days ago
Last synced at: 8 days ago
Commit Stats
Commits: 886
Authors: 14
Mean commits per author: 63.29
Development Distribution Score: 0.024
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/WenjieDu/PyPOTS
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
Funding Links https://github.com/sponsors/WenjieDu