def METHOD_NAME():
    # SEASONALITY GLOBAL LOCAL MODELLING - NO EXOGENOUS VARIABLES
    log.info("Global Modeling + Global Normalization")
    df = pd.read_csv(PEYTON_FILE, nrows=512)
    df1_0 = df.iloc[:128, :].copy(deep=True)
    df1_0["ID"] = "df1"
    df2_0 = df.iloc[128:256, :].copy(deep=True)
    df2_0["ID"] = "df2"
    df3_0 = df.iloc[256:384, :].copy(deep=True)
    df3_0["ID"] = "df3"
    m = NeuralProphet(
        n_forecasts=2,
        n_lags=10,
        growth="discontinuous",
        epochs=EPOCHS,
        batch_size=BATCH_SIZE,
        learning_rate=LR,
        season_global_local="glocsl",
        trend_global_local="glocsl",
    )
    train_df, test_df = m.split_df(pd.concat((df1_0, df2_0, df3_0)), valid_p=0.33, local_split=True)
    m.fit(train_df)
    future = m.make_future_dataframe(test_df)
    m.predict(future)
    m.test(test_df)
    m.predict_trend(test_df)
    m.predict_seasonal_components(test_df)