Deep Representation Learning for Prediction of Temporal Event Sets in
  the Continuous Time Domain
Parag Dutta Kawin Mayilvaghanan Pratyaksha Sinha Ambedkar Dukkipati
Temporal Point Processes (TPP) play an important role in predicting or
forecasting events. Although these problems have been studied extensively,
predicting multiple simultaneously occurring events can be challenging. For
instance, more often than not, a patient gets admitted to a hospital with
multiple conditions at a time. Similarly people buy more than one stock and
multiple news breaks out at the same time. Moreover, these events do not occur
at discrete time intervals, and forecasting event sets in the continuous time
domain remains an open problem. Naive approaches for extending the existing TPP
models for solving this problem lead to dealing with an exponentially large
number of events or ignoring set dependencies among events. In this work, we
propose a scalable and efficient approach based on TPPs to solve this problem.
Our proposed approach incorporates contextual event embeddings, temporal
information, and domain features to model the temporal event sets. We
demonstrate the effectiveness of our approach through extensive experiments on
multiple datasets, showing that our model outperforms existing methods in terms
of prediction metrics and computational efficiency. To the best of our
knowledge, this is the first work that solves the problem of predicting event
set intensities in the continuous time domain by using TPPs.