Building NARX models using general estimators¶

pip install sysidentpy

import matplotlib.pyplot as plt
from sysidentpy.metrics import mean_squared_error
from sysidentpy.utils.generate_data import get_siso_data
from sysidentpy.general_estimators import NARX
from sklearn.linear_model import BayesianRidge, ARDRegression
from sklearn.ensemble import GradientBoostingRegressor
from catboost import CatBoostRegressor

from sysidentpy.basis_function._basis_function import Polynomial, Fourier
from sysidentpy.utils.plotting import plot_residues_correlation, plot_results
from sysidentpy.residues.residues_correlation import (
    compute_residues_autocorrelation,
    compute_cross_correlation,
)

# simulated dataset
x_train, x_valid, y_train, y_valid = get_siso_data(
    n=10000, colored_noise=False, sigma=0.01, train_percentage=80
)

catboost = CatBoostRegressor(iterations=300, learning_rate=0.1, depth=6)

gb = GradientBoostingRegressor(
    loss="quantile",
    alpha=0.90,
    n_estimators=250,
    max_depth=10,
    learning_rate=0.1,
    min_samples_leaf=9,
    min_samples_split=9,
)

def plot_results_tmp(y_valid, yhat):
    _, ax = plt.subplots(figsize=(14, 8))
    ax.plot(y_valid[:200], label="Data", marker="o")
    ax.plot(yhat[:200], label="Prediction", marker="*")
    ax.set_xlabel("$n$", fontsize=18)
    ax.set_ylabel("$y[n]$", fontsize=18)
    ax.grid()
    ax.legend(fontsize=18)
    plt.show()