WenjieDu/PyPOTS

Health v2 · maintenance only

Adoption not part of the score

2072 stars · 191 forks observed · 2026-10-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyPOTS is a Python toolbox for machine learning and data mining on partially-observed time series with missing values. It integrates 50+ state-of-the-art neural network models for imputation, classification, clustering, forecasting, and anomaly detection with a unified fit/predict API built on PyTorch.

Use cases

• impute missing values in multivariate time series with NaNs
• forecast irregularly-sampled time series with missing data
• classify partially-observed time series
• cluster incomplete time series data
• detect anomalies in sensor data with missing readings
• benchmark SOTA models on incomplete time series datasets

When to choose

• your time series data has missing values (NaNs) from sensor failures or irregular sampling
• you want a unified scikit-learn-like API to try many SOTA imputation/forecasting/classification models
• you work in healthcare, IoT, climate, or industrial domains with incomplete multivariate time series
• you want reproducible, peer-reviewed implementations with benchmarks

When to avoid

• your time series data is fully observed with no missing values
• you need streaming/real-time inference rather than batch analysis
• you need a framework outside Python/PyTorch
• you need classical statistical methods only (e.g., simple ARIMA) without deep learning

Facets

library · maturity active

machine-learning deep-learning data-science nlp machine-learning data-science time-series artificial-intelligence python time-series missing-data imputation anomaly-detection forecasting pytorch partially-observed-time-series clustering classification

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