def METHOD_NAME(dataset: str, group: str) -> None:
    train, horizon, freq, seasonality = get_data('data', dataset, group)
    partial_fit_and_predict = partial(fit_and_predict, 
                                      horizon=horizon, freq=freq, seasonality=seasonality)
    start = time.time()
    print(f'Parallelism on {cpu_count()} CPU')
    with Pool(cpu_count()) as pool:
        results = pool.starmap(partial_fit_and_predict, train.groupby('unique_id'))
    end = time.time()
    print(end - start)
    forecasts = pd.concat(results)
    forecasts.to_csv(f'data/statsmodels-forecasts-{dataset}-{group}-pred-int.csv', index=False)
    time_df = pd.DataFrame({'time': [end - start], 'model': ['ets_statsmodels']})
    time_df.to_csv(f'data/statsmodels-time-{dataset}-{group}-pred-int.csv', index=False)