pykalman/pykalman

Health v2 · maintenance only

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Adoption not part of the score

1329 stars · 403 forks observed · 2026-08-28

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

pykalman is a Python library implementing the Kalman Filter, Kalman Smoother, Unscented Kalman Filter, and EM algorithm for state estimation in time series. It supports linear dynamical systems modeling and parameter learning for sequential data.

Use cases

• estimate hidden states from noisy sensor time series
• smooth noisy GPS or IMU trajectories
• fit linear dynamical system parameters with EM
• run an unscented Kalman filter for nonlinear systems
• filter and forecast sequential data in Python
• learn transition and observation covariance matrices from data

When to choose

• you need a simple, well-documented Kalman filter/smoother in Python
• you want EM-based parameter estimation for linear dynamical systems
• your system is linear or mildly nonlinear (UKF suffices)

When to avoid

• you need real-time or embedded performance in C/C++
• you need particle filters or general nonlinear state-space models
• you need deep-learning-based sequence models

Facets

library · maturity active

machine-learning math data-science machine-learning data-science time-series python kalman-filter state-estimation em-algorithm time-series unscented-kalman-filter signal-processing algorithms

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