Models, approaches, for making repeated predictions over the duration of an event Say I have some events and I would like to predict an event outcome. For each event the predictors get populated over the duration of the event. For example an event may last 3 hours with predictors A, B, and C arriving at 10 minutes and predictors D, and E, arriving at 60 minutes. I would like to make predictions at both time points utilizing the available predictors. The idea being that as new information is comes in better predictions get made.
The data I have to train on is the 'state' of the events. Each row in the dataset represents updates to an event, i.e., when new information arrived or existing information changed. A constraint is that the times when predictors are available is not the same for all events.
Are there models that can handle this? Or approaches to this kind of problem? I am at a loss for what this kind of problem is called? Thank you.

It's some sort of a panel data. You can define a N/A like value of your choice (e.g. -999 if numeric) for the data that's not available. Tree based algorithms (e.g. random forests) get along well with that sort of information.
Each row can be a training sample. The IID assumption of samples is violated but this is also the case in most time series designs. Note also that your validation and test sets should respect the time order of your samples. Ideally, you can split by events (e.g. event id) such that training, validation and test sets don't contain the same events at different stages to prevent data leakage.
