The way around it at the moment is to use the same “ML Training End” date. This option appears when you choose “Entire Dataset”.
The problem with our current solution is that you will need to download more and more data for predictions. For example if your model was trained using 10 years of data and 4 years of validation, when you do predictions a week later you have to download 14 years + 1 week and use the exact same “ML Training End”. From this download you only use the new, single date, that has been normalized using the same stats as the original training.
It’s very inefficient and costly. We planned to store the mean and std for re-use but wanted to hear some feedback first.