Sysidentpy

CONTRIBUTORS

- wilsonrljr

CHANGES

- MAJOR: n-steps-ahead prediction.
- Now you can define the numbers of steps ahead in the predict function.
- Only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.

- MAJOR: Simulating predefined models.
- Added the new class SimulatePolynomialNarmax to handle the simulation of known model structures.
- Now you can simulate predefined models by just passing the model structure codification. Check the notebook examples.

- Added 4 new notebooks in the example section.

- Added iterative notebooks. Now you can run the notebooks in Jupyter notebook section of the documentation in Colab.

- Fix code format issues.

- Added new tests for SimulatePolynomialNarmax and generate_data.

- Started changes related to numpy 1.19.4 update. There are still some Deprecation warnings that will be fixed in next update.

- Fix issues related to html on Jupyter notebooks examples on documentation.

- Updated Readme with examples of how to use.

CONTRIBUTORS

- wilsonrljr

CHANGES

- MAJOR: Introducing NARX Neural Network in SysIdentPy.
- Now you can build NARX Neural Network on SysIdentPy.
- This feature is built on top of Pytorch. See the docs for more details and examples of how to use.

- MAJOR: Introducing general estimators in SysIdentPy.
- Now you are able to use any estimator that have Fit/Predict methods (estimators from Sklearn and Catboost, for example) and build NARX models based on those estimators.
- We use the core functions of SysIdentPy and keep the Fit/Predict approach from those estimators to keep the process easy to use.
- More estimators are coming soon like XGboost.

- Added notebooks to show how to build NARX neural Network.

- Added notebooks to show how to build NARX models using general estimators.

- Changed the default parameters of the plot_results function.

- NOTE: We will keeping improving the Polynomial NARX models (new model structure selection algorithms and multiobjective identification
is on our roadmap). These recent modifications will allow us to introduce new NARX models like PWARX models very soon.

- New template for the documentation site.

- Fix issues related to html on Jupyter notebooks examples on documentation.

- Updated Readme with examples of how to use.

CONTRIBUTORS

- wilsonrljr
- renard162

CHANGES

- Fixed a bug concerning the xlag and ylag in multiple input scenarios.
- Refactored predict function. Improved performance up to 87% depending on the number of regressors.
- You can set lags with different size for each input.
- Added a new function to get the max value of xlag and ylag. Work with int, list, nested lists.
- Fixed tests for information criteria.
- Added SysIdentPy logo.
- Refactored code of all classes following PEP 8 guidelines to improve readability.
- Added Citation information on Readme.
- Changes on information Criteria tests.
- Added workflow to run the tests when merge branch into master.
- Added new site domain.
- Updated docs.