Open Source Packages (in portuguese)
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Companies and Universities using SysIdentPy!
SysIdentPy is a simple and easy to learn System Identification library in Python!
Quick & Easy model building
SysIdentPy makes forecasting easy and powerful. Its intuitive interface and extensive selection of methods, including Polynomial NARMAX and NARX Neural Networks, allow you to create robust nonlinear dynamic models with ease. Whether you're new to forecasting or an experienced practitioner, SysIdentPy gives you the flexibility and control you need to achieve accurate results. With SysIdentPy, forecasting has never been easier or more efficient.
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Model Validation with Essential Features
You can simulate pre-defined models, compare identified models, analyze their properties, and validate them against test datasets.
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Robust documentation and examples
There are tons of interesting examples to help you learn SysIdentPy. You can start with our official tutorials right now!
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Cool facts about SysIdentPy
SysIdentPy empowers anyone to build low-code and robust dynamic models from input and output data.
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Meet Our Developers
Wilson Rocha
Main Developer and Maintainer
Users sharing their experience
The greatest thing about SysidentPy is that it solves complex problems in a simple and elegant way. Also it has great performance and is very user friendly. We have it now running in production.
SysIdentPy is an great to work with time series and dynamic systems, providing native methods and supporting many different estimators from packages like sklearn and Catboost to build different NARMAX models.
In my experience, SysIdentPy is the best python package for System Identification which uses NARMAX models.
SysidentPy is a high-performance solution that can be used in highly challenging scenarios for non-linear dynamic modeling. At Technium - IA For EveryOne we recommend its use in our projects..
