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

TL;DR

This paper introduces a novel, scalable method using Temporal Point Processes to predict multiple simultaneous events in continuous time, addressing a complex open problem in event set forecasting.

Contribution

The work presents the first approach to predict event set intensities in continuous time using TPPs, incorporating contextual embeddings and domain features for improved accuracy.

Findings

Outperforms existing methods in prediction accuracy

Demonstrates computational efficiency on multiple datasets

Addresses the challenge of predicting simultaneous events in continuous time

Abstract

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.