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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