Hello everyone! I want to create a one model per step where each model predicts the next 21 days in increments of 7 days. I wasn't sure about the interaction between lightgbm.cv and max_horizon. I was wondering if someone could let me know whether there is an example for this case. ( I couldn't find one on GitHub or the main page ) lgb_cv = LightGBMCV( freq='d', lags=[7, 14], lag_transforms={ 7 : [ ExpandingMean(), ] ) cv_hist = lgb_cv.fit( sales_pd, n_windows=4, h=21, dropna=False, metric=comp_loss, num_iterations=10_000, params={'verbosity': -1, 'learning_rate': 0.2, 'num_leaves': 128}, early_stopping_evals=5, ) mlf = MLForecast.from_cv(lgb_cv) mlf.fit(sales, dropna=False) preds = mlf.predict(h=7,max_horizon=21)

Hey. I think LightGBMCV doesnt support the direct approach, just recursive