PySINDy
PySINDy: A comprehensive Python package for robust sparse system identification - Published in JOSS (2022)
SysIdentPy
SysIdentPy: A Python package for System Identification using NARMAX models - Published in JOSS (2020)
autokoopman
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
https://github.com/baggepinnen/lowlevelparticlefilters.jl
State estimation, smoothing and parameter estimation using Kalman and particle filters.
https://github.com/baggepinnen/controlsystemidentification.jl
System Identification toolbox, compatible with ControlSystems.jl
ceem
Certainty-Equivalent Expectation Maximization: a scalable algorithm for system identification of partially observed systems
https://github.com/biaslab/acc2022-vmpnarmax
Experiments and derivations for ACC2022 paper on variational message passing for online NARMAX identification.
https://github.com/biaslab/iwai2020-onlinesysid
Code and experiments for IWAI 2020 submission on online system identification
https://github.com/biaslab/nsi-silverbox
Online system identification in Silverbox by minimising free energy
https://github.com/baggepinnen/lpvspectral.jl
Least-squares (sparse) spectral estimation and (sparse) LPV spectral decomposition.
https://github.com/biaslab/ccta2024-bidconvection
Experiments for CCTA 2024 submission on fast Bayesian gray-box identification of convection in heat transfer dynamics.
https://github.com/baggepinnen/ltvmodels.jl
Tools to estimate Linear Time-Varying models in Julia
NonSysId: Nonlinear System Identification with Improved Model Term Selection for NARMAX Models
NonSysId: Nonlinear System Identification with Improved Model Term Selection for NARMAX Models - Published in JOSS (2025)
deepsysid
System identification toolkit for multistep prediction using deep learning and hybrid methods.
mtrf-toolbox
A MATLAB package for modelling multivariate stimulus-response data
https://github.com/biaslab/cdc-2022
Experiments and derivations for CDC2022 paper on message passing-based inference for NARMAX system identification.
https://github.com/equinor/timeseriesanalysis
Library that combines control engineering, dynamic simulation and machine learning on time-series. Developed to describe industrial processes and -automation. Lightweight, robust and fast for use in advanced analytics. Built on .NET to run anywhere.