Repeated k-fold Cross Validation for time series data?

I have a relative small sample size (330 with 45 features) + it’s time series data.

I want to train my LightGBM regression model for best generalized RMSE score and want to use repeated CV. I use hyperopt to do hyperparameter optimization to optimize for lowest RMSE.

The last 2 months include data post-corona, so a standard k-fold CV would probably fail when testing on such data because there was quite a shift in the target variable y.

Standard ways to do repeated CV use resample / reshuffle which is not useable with time series data.

What is best practice in this case? How can I do repeated Cross Validation while working with time series data?

At the moment I do this for the fmin() function.

def lightgbm_cv_repeated(params):
    params = {
        'n_estimators': int(params['n_estimators']), 
        'max_depth': int(params['max_depth']), 
        'learning_rate': params['learning_rate'],
        'min_child_samples': int(params['min_child_samples']),
        'min_child_weight': params['min_child_weight'],
        'feature_fraction': params['feature_fraction'],
        'bagging_fraction': params['bagging_fraction'],
        'bagging_freq': int(params['bagging_freq']),
        'num_leaves': int(params['num_leaves']),
        'max_bin': int(params['max_bin']),
        'num_iterations': int(params['num_iterations']),
        'objective': 'rmse',
        }

    scores = []

    for i in range(5, 11):
        print(i)
        cvTSS = TimeSeriesSplit(max_train_size=None, n_splits=i)
    
        model = lgb.LGBMRegressor(random_state=i, **params)
        score = -cross_val_score(model, X=X, y=y, cv=cvTSS, scoring="neg_root_mean_squared_error", n_jobs=-1).mean()
        scores.append(score)

    result = mean(scores)
    return result