  Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Units
(GRUs) and transformers have been developed to classify time series data with
the assumption that events in a sequence are ordered. On the other hand, fewer
models have been developed for set based inputs, where order does not matter.
There are several use cases where data is given as partially-ordered sequences
because of the granularity or uncertainty of time stamps. We introduce a novel
transformer based model for such prediction tasks, and benchmark against
extensions of existing order invariant models. We also discuss how transition
probabilities between events in a sequence can be used to improve model
performance. We show that the transformer-based equal-time model outperforms
extensions of existing set models on three data sets.
