def METHOD_NAME():
    df = pd.read_csv(AIR_FILE)
    m = NeuralProphet(seasonality_mode="multiplicative")
    df_train, df_test = m.split_df(df=df, freq="MS", valid_p=0.2)
    system_speed, std = get_system_speed()
    start = time.time()
    metrics = m.fit(df_train, validation_df=df_test, freq="MS", early_stopping=True)
    end = time.time()
    accuracy_metrics = metrics.to_dict("records")[-1]
    accuracy_metrics["time"] = round(end - start, 2)
    accuracy_metrics["system_performance"] = round(system_speed, 5)
    accuracy_metrics["system_std"] = round(std, 5)
    with open(os.path.join(DIR, "tests", "metrics", "AirPassengers.json"), "w") as outfile:
        json.dump(accuracy_metrics, outfile)
    create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "AirPassengers.svg"))