Implementation of the N4SID algorithm for subspace identification [1], together with Kalman filtering and state-space models.
State-space models are versatile models for representing multi-dimensional timeseries. As an example, the ARMAX(p, q, r)-models - AutoRegressive MovingAverage with eXogenous input - are included in the representation of state-space models. By extension, ARMA-, AR- and MA-models can be described, too. The numerical implementations are based on [2].
Releases are made available on PyPi.
The recommended installation method is via pip:
pip install nfoursid
For a development setup, the requirements are in dev-requirements.txt.
Subsequently, this repo can be locally pip-installed.
Documentation is provided here. An example Jupyter notebook is provided here.
FAQs
Implementation of N4SID, Kalman filtering and state-space models
We found that nfoursid demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.