Python - Arima forecasting returning incomplete informations

Question

I tried to use the function pmdarima.auto_arima following a tutorial on my data. Despite having success on the tutorial, the same code on my data don't return forecasting values but does return upper and lower series.

Here is my code :

from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib.pylab as plt

from matplotlib.pylab import rcParams

from statsmodels.tsa.stattools import adfuller

import pmdarima as pm


df = pd.read_csv("Test_Data.csv",sep=";")

#string to date format
df['Month'] = pd.to_datetime(df['Month'], format="%d/%m/%Y")
df = df.set_index(['Month'])



def forecast(ARIMA_model, periods=24):
    # Forecast
    n_periods = periods
    fitted, confint = ARIMA_model.predict(n_periods=n_periods, return_conf_int=True)
    index_of_fc = pd.date_range(df.index[-1] + pd.DateOffset(months=1), periods = n_periods, freq='MS')

    # make series for plotting purpose
    fitted_series = pd.Series(fitted, index=index_of_fc)
    lower_series = pd.Series(confint[:, 0], index=index_of_fc)
    upper_series = pd.Series(confint[:, 1], index=index_of_fc)

    # Plot
    plt.figure(figsize=(15,7))
    plt.plot(df["NET"], color='#1f76b4')
    plt.plot(fitted_series, color='darkgreen')
    plt.fill_between(lower_series.index, 
                    lower_series, 
                    upper_series, 
                    color='k', alpha=.15)

    plt.title("ARIMA/SARIMA - Forecast of Airline Passengers")
    plt.show()


# Seasonal - fit stepwise auto-ARIMA
SARIMA_model = pm.auto_arima(df["NET"], start_p=1, start_q=1,
                         test='adf',
                         max_p=3, max_q=3, 
                         m=12, #12 is the frequncy of the cycle
                         start_P=0, 
                         seasonal=True, #set to seasonal
                         d=None, 
                         D=1, #order of the seasonal differencing
                         trace=False,
                         error_action='ignore',  
                         suppress_warnings=True, 
                         stepwise=True)



forecast(SARIMA_model)

And here is the result : No display of the forecasted value

here is the data format used :