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Temporal Point Processes (TPP) play an important role in predicting orforecasting events. Although these problems have been studied extensively,predicting multiple simultaneously occurring events can be challenging. Forinstance, more often than not, a patient gets admitted to a hospital withmultiple conditions at a time. Similarly people buy more than one stock andmultiple news breaks out at the same time. Moreover, these events do not occurat discrete time intervals, and forecasting event sets in the continuous timedomain remains an open problem. Naive approaches for extending the existing TPPmodels for solving this problem lead to dealing with an exponentially largenumber of events or ignoring set dependencies among events. In this work, wepropose a scalable and efficient approach based on TPPs to solve this problem.Our proposed approach incorporates contextual event embeddings, temporalinformation, and domain features to model the temporal event sets. Wedemonstrate the effectiveness of our approach through extensive experiments onmultiple datasets, showing that our model outperforms existing methods in termsof prediction metrics and computational efficiency. To the best of ourknowledge, this is the first work that solves the problem of predicting eventset intensities in the continuous time domain by using TPPs.

Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time Domain