def METHOD_NAME():
    log.info("testing: Naive Split Conformal Prediction Air Travel")
    df = pd.read_csv(AIR_FILE)
    m = NeuralProphet(
        seasonality_mode="multiplicative",
        loss_func="MSE",
        quantiles=[0.05, 0.95],
        epochs=EPOCHS,
        batch_size=BATCH_SIZE,
        learning_rate=LR,
    )
    train_df, test_df = m.split_df(df, freq="MS", valid_p=0.2)
    train_df, cal_df = m.split_df(train_df, freq="MS", valid_p=0.15)
    m.fit(train_df, freq="MS")
    alpha = 0.1
    decompose = False
    for method in ["naive", "cqr"]:  # Naive and CQR SCP methods
        for show_all_PI in [True, False]:
            future = m.make_future_dataframe(
                test_df,
                periods=50,
                n_historic_predictions=len(test_df),
            )
            forecast = m.conformal_predict(
                future,
                calibration_df=cal_df,
                alpha=alpha,
                method=method,
                decompose=decompose,
                show_all_PI=show_all_PI,
            )
            uncertainty_evaluate(forecast)