Azure ML / AutoML: problem with univariate time series forecasting

I'm having troubles generating univariate time series forecasts with Azure Automated Machine Learning (I know...).

What I'm doing

So I have about 5 years worth of monthly observations in a dataframe that looks like this:

date target_value
2015-02-01 123
2015-03-01 456
2015-04-01 789
... ...

I want to forecast target_value based on past values of target_value, i.e. univariate forecasting like ARIMA for instance.
So I am setting up the AutoML forecast like this:

# that's the dataframe as shown above
train_data = Dataset.Tabular.from_delimited_files(path=datastore.path(my_remote_filename))

# ...other code...

forecasting_parameters = ForecastingParameters(
time_column_name='date',
forecast_horizon=2,
target_lags='auto',
freq='MS'
)

debug_log='automl_forecasting_function.log',
primary_metric='normalized_root_mean_squared_error',
enable_dnn=True,
experiment_timeout_hours=8.0,
enable_early_stopping=True,
training_data=train_data,
compute_target='my-cluster',
n_cross_validations=3,
verbosity=logging.INFO,
max_concurrent_iterations=4,
max_cores_per_iteration=-1,
label_column_name='target_value',
forecasting_parameters=forecasting_parameters)


What the problem is

But AutoML does not seem to generate the forecast for target_value based on past values of target_value. It seems to use the date column as the independent variable! The feature importance chart also shows date as the input feature:

As a side note: running multivariate forecasts works fine.
When I use a dataset like this, feature_1 and feature_2 are used (i.e. as the X) to forecast target_value (i.e. the y)

date feature_1 feature_2 target_value
2015-02-01 10 7 123
2015-03-01 30 2 456
2015-04-01 20 5 789
... ... ... ...

My questions therefore
How do I need to set up a univariate AutoML forecast to forecast target_value based on past observations target_value?
I assumed generating lagged values for target_value etc. is exactly what AutoML is supposed to do.

Thanks!