# pykalman/pykalman

Kalman Filter, Smoother, and EM Algorithm for Python

Repository: https://github.com/pykalman/pykalman
Canonical: https://ross.abutalabs.com/products/pykalman
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-04-20T19:22:17+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 78, release rhythm 56, longevity 100
- inputs: {"age_days": 5140, "days_push": 135, "days_rel": 214, "gap_med": 33, "n_releases_24m": 6}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1329, forks 403 (observed 2026-08-28T04:04:23.921665+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, math, data-science
- domain: machine-learning, data-science, time-series
- platform: python
- tags: kalman-filter, state-estimation, em-algorithm, time-series, unscented-kalman-filter, signal-processing, algorithms

## Member repositories
- pykalman/pykalman (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:23.921665+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:45:42.399908+00:00, confidence not recorded.
  - readme: https://github.com/pykalman/pykalman (fetched 2026-08-28T04:04:23.921665+00:00, sha 496b6ca690b3)
  - registry_pypi: https://pypi.org/pypi/pykalman/json (fetched 2026-08-29T12:04:45.911522+00:00, sha 497d95bae878)
- Data as of 2026-08-30T08:39:29.467469+00:00.