Training a DeepAR time series model with monthly data. Can only train on Daily

I am training a DeepAR model (arXiv) in Jupyter Notebook. I am following this tutorial.

I create a collection of time series (concat_df), as needed by the DeepAR method:

Each row is a time series. This collection is used to train the DeepAR model. The input format expected by DeepAr is a list of series. So I create this from the above data frame:

time_series = []
for index, row in concat_df.iterrows():
    time_series.append(row)

With this list of time series I then set the freq, prediction_length and context_length (note I am setting frequency to Daily in this first example):

freq = D
prediction_length = 3
context_length = 3

...as well as the T-Naught, Data Length, Number of Time Series and Period:

t0 = concat_df.columns[0]
data_length = concat_df.shape[1]
num_ts = concat_df.shape[0]
period = 12

I create the training set:

time_series_training = []
for ts in time_series:
    time_series_training.append(ts[:-prediction_length])

..and visualize this with the test set:

time_series[0].plot(label=test)
time_series_training[0].plot(label=train, ls=:)
plt.legend()
plt.show()

So far so good, everything seems to agree with the tutorial.

I then use the remaining code to invoke the deployed model as explained in the referenced article:

list_of_df = predictor.predict(time_series_training[:5], content_type=application/json)

BUT, if I change the frequency to monthly (freq = M) I get the following error:

ValueError: Units 'M', 'Y', and 'y' are no longer supported, as they do not represent unambiguous timedelta values durations.

Why would monthly data not be accepted? How can I train monthly data on DeepAR? Is there a way to specify some daily equivalent to monthly data?

This appears to be some kind of Pandas error, as shown here.