Setup: We have sequence of events that are not evenly spaced (not a time series). Length of the sequence is constant.
Goal: Predict class of the event that is most probable to follow this sequence.
Background: I know that RNN would probably a good fit for this task, but at the same time I wonder whether parameters sharing in our U,W,V matrices actually hurt accuracy ( even though training process is cheaper). Let's say we are ok to spent more time(and data) for training and don't want to compromise accuracy.
Question: Is it true that by using regular MLP we can achieve better or at least same performance if we just combine/flatten all features from those sequence events and pass them alltogether as an input? I believe model should still be able to learn interactions between features(that represent different events in a sequence) but not sure how good it will be at it and if not then why?