def METHOD_NAME():
    df = pd.read_csv(YOS_FILE)
    m = NeuralProphet(
        n_lags=24,
        n_forecasts=24,
        changepoints_range=0.95,
        n_changepoints=30,
        weekly_seasonality=False,
    )
    df_train, df_test = m.split_df(df=df, freq="5min", valid_p=0.2)
    system_speed, std = get_system_speed()
    start = time.time()
    metrics = m.fit(df_train, validation_df=df_test, freq="5min", 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", "YosemiteTemps.json"), "w") as outfile:
        json.dump(accuracy_metrics, outfile)
    create_metrics_plot(metrics).write_image(os.path.join(DIR, "tests", "metrics", "YosemiteTemps.svg"))