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WPyPOTS
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Popularity
Repository stars2,09167,972Highest
7-day net stars——
Fork19427,626Highest
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2026-10-312026-11-02
Maintenance notesNot verifiedUnknown
Repository created2022-03-292010-08-17
License & use
LicenseBSD-3-Clause · PermissiveBSD-3-Clause · Permissive
PricingPricing not verifiedPricing not verified
Basics
Browse by purposeData analysisData analysis
TypeFrameworks & SDKsFrameworks & SDKs
SubcategoryData science & machine learningData science & machine learning
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Verify software, hosting, model and infrastructure costs against the current official documentation.
Pricing source——
First collected2026-11-032026-11-02
MetricRepository stars, not unique users or individual-skill installsRepository Stars, not unique users
LanguagePythonPython
About this projectA Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing valuesMachine-learning algorithms and modeling tools for Python.
Measured at2026-11-02 19:00 UTC2026-11-02 19:20 UTC
License tiers are derived from metadata. Check obligations for your use; unknown does not mean prohibited. Ranked by repository stars, one position per repository. Stars and forks are not user counts.