def make_windows(feature_rows, target_values, lookback):
    windows = []
    targets = []
    target_days = []
    for start in range(len(feature_rows) - lookback):
        target_day = start + lookback
        windows.append(feature_rows[start:target_day])
        targets.append(target_values[target_day])
        target_days.append(target_day)
    return (
        np.asarray(windows, dtype="float32"),
        np.asarray(targets, dtype="float32"),
        np.asarray(target_days, dtype="int32"),
    )


x_all, y_all, target_days = make_windows(features, demand_scaled, LOOKBACK)
train_mask = target_days < TRAIN_END
validation_mask = (target_days >= TRAIN_END) & (target_days < VALIDATION_END)
holdout_mask = target_days >= VALIDATION_END

x_train, y_train = x_all[train_mask], y_all[train_mask]
x_val, y_val = x_all[validation_mask], y_all[validation_mask]
x_holdout, y_holdout = x_all[holdout_mask], y_all[holdout_mask]
holdout_days = target_days[holdout_mask]