AutoML: problem with univariate time series forecasting

Question

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'  
)  
  
automl_config = AutoMLConfig(task='forecasting',  
                             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!

Answer 1

@Anonymous Thanks, AutoML does use the date column as an independent variable. We engineer several features from it, this is a standard practice for learning seasonal patterns. In the given scenario the date column will be featurized to represent 'day', 'month', 'day of week' etc. This is done to train regression-based model on this data, which will use the generated columns for prediction.

Please remove the target_lags='auto' to allow selection of Arima. We have to block certain models (e.g. Arima) when the target lags are set. This is a product gap that we're in the process of fixing.