Google Vertex Auto-ML Forecast every 30 mins predict 2 hours - time-series

Question

GOAL
Every 30 mins I get a new bunch of price related data:
CurrentDatetime, CurrentPrice, Feature1, Feature2
I want to predict the price in 2 hours from now, so 4x30mins (4 steps into the future)
PROBLEM DESCRIPTION
I am puzzled what google vertex auto-ml forecasting is doing and if I can trust the results I am getting. Also unsure how to use a trained model for batch predicting.
WHAT I DID
I think the way to set up the training dataset is to add:
TargetDateTime column (2 hours ahead of CurrentDatetime)
TargetActualPrice column (the actual price 2 hours into the future)
TimeSeriesId column (always equal to 1 as all the data is one time
series).
This means, every 30 mins I now have:
CurrentDatetime, CurrentPrice, Feature1, Feature2, TargetDateTime, TargetActualPrice, TimeSeriesId
I use this dataset to train an auto-ml forecast model, setting:
"series Identifier Column" to TimeSeriesId,
"Target Column" to TargetActualPrice,
"Timestamp Column" to TargetDateTime
"Data granularity" to 30mins
"Forecast Horizon" to 4
"Context Window" to 4 (use last 2 hours of historic data to predict next 2 hours)
Split train/val/test chronologically (on TargetDateTime as is Timestamp column)
This model trains and gives some results.
Looking at the saved test data set, I can see 4 rows for each TargetDateTime, with a predictedvalue column containing a price prediction and a predicted_on_TargetDateTime column which goes from CurrentDateTime to TargetDateTime in 30 mins intervals.
This makes sense, for every 30 mins of input data, the model makes 4 predictions, each 30 mins into the future, ending up with a prediction 2 hours into the future. Great.
PROBLEM 1 : Batch predictions
I get confused when I try to use this trained model to make batch predictions. The crux of the problem is that Vertex will look at the batch input dataset, find the first row (30 min input data) for which there is no actual price data yet (TargetActualPrice is null) and then predict the next 4 steps (2 hours). This seems to mean, to make a next prediction, I would need to wait for the actuals of the previous prediction. But that means, when I get the next set of input data (30 mins later, and 1.30 hrs out from previous prediction target), I cannot use the model to make a new prediction cause the previous prediction has not TargetActualPrice yet.
To make it more explicit, suppose I have the following batch data:
CurrentDatetime
CurrentPrice
Feature1
Feature2
TargetDateTime
TargetActualPrice
TimeSeriesId
11:00
$2.1
3.4
abc
13:00
$2.4
1
11:30
$2.2
3.3
abd
13:30
$2.5
1
12:00
$2.3
3.1
abe
14:00
$2.6
1
12:30
$2.3
3.0
abe
14:30
$2.7
1
13:00
$2.4
2.9
abf
15:00
null
1
13:30
$2.5
2.8
abg
15:30
null
1
14:00
$2.6
2.7
abh
16:00
null
1
14:30
$2.7
2.6
abi
16:30
null
1
In the batch data above, I have 2 hours (4 rows) of historic data with actuals (11:00-12:30). Current time is 14:30 so I don't have actuals for 15:00 yet. The last prediction made was with the 13:00 input data (as it is the first row with actual data = null). The 13:30 - 14:30 rows I cannot use for a new prediction until I have the 15:00 actuals.
This doesn't make sense to me. I should be able to make a new 4 hour prediction every 30 mins? I must be doing something wrong?
Is the solution that, when I get the next 30 mins of input data, should I put the last predicted value into the actuals column (and update with real actuals once I have it) to proceed with next prediction? Seems cumbersome.
PROBLEM 2 : Leakage
My other concern with this is how Vertex is training and calculating the results. I am worried that when (during training) Vertex picks up the next row of 30 mins data, it will create a prediction based on the previous 4x30 mins of data (2 hour "Context window") INCLUDING the TargetActualPrice data for those rows. But this would be incorrect, as the TargetActualPrice value is 2 hours into the future and not yet available when the next 30 mins of data comes in. This would mean leakage of actual data, predicting using actuals before they are known (ie cheating).
SUMMARY
In summary, I am hoping someone can tell if I am setting the dataset up incorrectly, and/or how to batch predict every 30 mins.

Answer 1

With regards to my leakage concern, it originally came about because I didn't understand the batch predictions, and it seemed that every 30 mins I needed the price from 2 hours in the future in order to update the actuals of the previous prediction and then create a new prediction, which is obviously unknown at that moment.
Now my understanding is that during training, even though every 30 min timestep will have an actual price column for 4 hours in the future, the model will only use actual prices available at the moment of prediction. If making a prediction at 14.30, the model will use 14.30 data (and historic data before 14.30), and then make 4x30 mins predictions. These four predictions do not use nor are influenced by the 30 mins data after 14.30 (15.00, 15.30, 16.00, 16.30). Nor does it use the 16.30 actual price value which is the target column on 14.30 row of data.

Answer 2

I think I now understand the batch predictions.
Every 30 mins I get a new set of data (Feature1 and Feature2) as well the CurrentPrice. I just add this data to the batch table with TargetActualPrice set to NULL and TargetDateTime set to 2 hours in the future. I also update the TargetActualPrice in the previous row(s) in the batch table (with TargetDateTime equal to current time). Now I can run the model against this batch table and get a prediction for a max of 4 rows with TargetActualPrice = NULL.
For clarity, in the batch table, I end up with 4 TargetActualPrice = NULL rows, matching the prediction horizon of the model. When
I run the prediction, I will get 4 prediction values for these NULL rows.