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hey @Nasreddine D! Currently, mlforecast does not support automatic hyperparameter + feature tuning. Could you help us open an issue to work on that feature? https://github.com/Nixtla/mlforecast/issues/new/choose In the meantime, one option might be to use hyperopt or optuna to create a pipeline that receives different hyperaparmeters and features and optimizes them. Inside the function, you could include the

cross_validation

method to optimize the hyperparameters and features considering different windows.

Hi @fede (nixtla) (they/them), I have used Optuna to tune a Lasso model. I had the following idea (please let me know if it is relevant): 1. Extract Features from an univariate TS (lags, rolling windows, expanding windows) 2. Tune the

Alpha

hyperparameter of Lasso Model with a

cross_validation

3. This best model with best alpha, will automatically select best features (put a 0 coefficient on "useless" features) 4. Then I can train a LGBM model with the best features selected from above steps. When I performed the first 3 steps, the model selected the best alpha, but all features coefficient were at 0 (so basically no feature selected). Please find below the I used.

import optuna
from sklearn.linear_model import Lasso
import numpy as np

# Hyperparameter tuning
def objective(trial):
    alpha = trial.suggest_float('alpha', 0.00001, 1, log=True)

    models = [Lasso(alpha=alpha, random_state=0, max_iter=5000)]
    
    mlf = MLForecast(
                        models = models, 
                        freq = 1,
                        target_transforms=[Differences([12]), StandardScaler()],
                        lags=np.arange(1,37),
                        lag_transforms={
                                    1: [(rolling_mean, 12), (rolling_max, 12), (rolling_min, 12)],
                                    1: [(rolling_mean, 24), (rolling_max, 24), (rolling_min, 24)],
                                    1: [(rolling_mean, 6), (rolling_max, 6), (rolling_min, 6)],
                                    2: [(rolling_mean, 12), (rolling_max, 12), (rolling_min, 12)],
                                    2: [(rolling_mean, 24), (rolling_max, 24), (rolling_min, 24)],
                                    2: [(rolling_mean, 6), (rolling_max, 6), (rolling_min, 6)],
                                    3: [(rolling_mean, 12), (rolling_max, 12), (rolling_min, 12)],
                                    3: [(rolling_mean, 24), (rolling_max, 24), (rolling_min, 24)],
                                    3: [(rolling_mean, 6), (rolling_max, 6), (rolling_min, 6)],
                                    6: [(rolling_mean, 12), (rolling_max, 12), (rolling_min, 12)],
                                    6: [(rolling_mean, 24), (rolling_max, 24), (rolling_min, 24)],
                                    6: [(rolling_mean, 6), (rolling_max, 6), (rolling_min, 6)],
                                    12: [(rolling_mean, 12), (rolling_max, 12), (rolling_min, 12)],
                                    12: [(rolling_mean, 6), (rolling_max, 6), (rolling_min, 6)],
                    )

    crossvalidation_df = mlf.cross_validation(
                                                data=Y_ts,
                                                window_size=24,
                                                n_windows=30,
                                                step_size=1
                                            )
    
    cv_rmse = crossvalidation_df.groupby(['cutoff']).apply(lambda x: rmse(x['y'].values, x["Lasso"].values)).to_frame().mean()
    return cv_rmse

study_lasso = optuna.create_study(direction='minimize')
study_lasso.optimize(objective, n_trials=50)