QOS
sysidentpy
| QOS | sysidentpy | |
|---|---|---|
| 1 | 7 | |
| 4 | 313 | |
| - | - | |
| 4.6 | 7.8 | |
| 4 months ago | 29 days ago | |
| Assembly | Python | |
| - | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
QOS
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I'm giving out microgrants to open source projects for the third year in a row! Brag about your projects here so I can see them, big or small!
I've written an assembly language and made an emulator to emulate this CPU: https://github.com/QSmally/QCPU-CLI and I'm currently working on QOS, the operating system I want run which is mainly being based on Unix: https://github.com/QSmally/QOS
sysidentpy
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Contribuição em biblioteca open source
Link para o site da documentação: SysIdentPy - SysIdentPy
- sysidentpy: A Python Package For System Identification Using NARMAX Models
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I'm giving out microgrants to open source projects for the third year in a row! Brag about your projects here so I can see them, big or small!
I'm the only maintainer, but I keep including new features (some exclusives, like the algorithm I've developed in my thesis to create NARMAX models), improving the code and documentation, and fixing bugs.
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A Comparison of Time Series Model Forecasting
benchmark codes: Welcome to SysIdentPy’s documentation! — NARMAX models
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Criei uma biblioteca open source para previsão de séries temporais
github: wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models (github.com)
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Help with time series analysis
I built a package where you can build polynomial NARMAX models using the most used method for model selection of this class of models in the beckend. In addition, you can built NARX Neural Networks for forecasting problems (this is built on top of Pytorch) and you can use any model that have a fit/predict method (Catboost, any estimator from sklearn) in a NARX configuration to perform infinity-steps-ahead prediction. Maybe its worth a try. Here is the link of the package: GitHub - wilsonrljr/sysidentpy: A Python Package For System Identification Using NARMAX Models
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