Siso-Predictor

A python module for predicting the future behavior of a targeted criterion based on input/output profiles

Code Citation

DOI

DOI

Possible use

  • Forcasting future evolution of an uncontrolled variable from its past
  • Design a Data-Driven Nonlinear Model Predicive Control ## Required Packages numpy, matplotlib ## Contents
  • The siso_predictor module siso_predictor.py
  • The main.py file
  • A utility module generate_data.py that is used to generate the data for the test of the module ## Example of use
sol = learn_model(
    y=y,
    U=U,
    ydef=ydef,
    N=100,
    n_clusters=3,
    nJump=1,
    max_leaf_nodes=1200,
    test_size=0.33,
    validation_mode='all',
    plot=True
    )

where - y the output time series - U the input time series - ydef the map that defines the target indicator to predict - N the window width - n_clusters the number of cluster used in the predictor - nJump the jump size used when processing the data - max_leaf_nodes the maximum number of leaf nodes in the Random Forest predictor - test_size the test size used in the learning validation split of the data - validation_mode the visualisation mode of the result (‘all’, ‘learning’, ‘test’) - plot whether to plot the results or not.

Example of result