How can I structure learning for this kind of data set

I have about 250 events that can be classified as A or B.

Each of these events is a time series with 2880 data points (1 event / min for 48h). In each event, it's the progression of all these data points that leads to the classification. For each event in the set, I have done the classification as A or B.

I want to train a model that, given a partial event (for example, 1500 data points), can tell me if it is likely this event will be of A or B type.

So essentially my events are like (F#):

type Event =
    {
        Data: float list  // 2880 points from the time series
        EventType: char   // A or B 
    }


events: Dictionary<string, Event>()  // id and event data, 250 entries

and then I have partial data:

partial: float list  // may contain 1500 data points for example

and I want to know if it's going to be an A type or a B type and the confidence level.

How can this be organized?