How to forecast unseen future Time-Series data using an ARIMA model on Python?

I have built an ARIMA model using Time Series data. Data has been split to (Train and Test). The code below builds the ARIMA model, scores the Test dataset (of 15 observations) and calculates the MSE error for each of the 15 observations.

My question is, how can I use this ARIMA model in order to predict any given time in the future ? For example, I would like to predict the time series value for Date "2018-01-01" using this ARIMA model that has been built and tested.

At the moment my dataset is in this form (with 10794 instances ranging from years 1975-2017):

Date           Values
2017-06-04    -0.116141
2017-06-11    -0.116669
2017-06-18    -0.114020
2017-06-25    -0.109614

My code for building ARIMA and calculating the MSE error for Test set validation

size = int(len(ts_week_log) - 15)
train, test = ts_week_log[0:size], ts_week_log[size:len(ts_week_log)]
history = [x for x in train]
predictions = list()

print('Printing Predicted vs Expected Values...')
print('\n')
for t in range(len(test)):
    model = ARIMA(history, order=(2,1,1))
    model_fit = model.fit(disp=0)
    output = model_fit.forecast()
    yhat = output[0]
    predictions.append(float(yhat))
    obs = test[t]
    history.append(obs)
    print('predicted=%f, expected=%f' % (np.exp(yhat), np.exp(obs)))

error = mean_squared_error(test, predictions)

print('\n')
print('Printing Mean Squared Error of Predictions...')
print('Test MSE: %.6f' % error)

predictions_series = pd.Series(predictions, index = test.index)

I am suspecting that I need to use model_fit.forecast function, but I would like to know if this is indeed the correct one to use ?

Further, I would like to know if there exist any functionality in Python that allows for new data field generations to be predicted of, such as the (model.make_future_dataframe(periods=24, freq = 'm')) on Prophet Forecasting Library.