Bayesian Neural Hawkes Process for Event Uncertainty Prediction
This work addresses the need for better uncertainty quantification in event modeling for practical applications, representing an incremental advancement by combining existing Bayesian and neural network approaches.
The authors tackled the problem of poor uncertainty quantification in neural point process models for event-time prediction by proposing the Bayesian Neural Hawkes Process (BNHP), which integrates Bayesian uncertainty modeling with neural networks and includes spatio-temporal capabilities, resulting in significant improvements in prediction performance and uncertainty quantification on simulated and real-world datasets.
Event data consisting of time of occurrence of the events arises in several real-world applications. Recent works have introduced neural network based point processes for modeling event-times, and were shown to provide state-of-the-art performance in predicting event-times. However, neural point process models lack a good uncertainty quantification capability on predictions. A proper uncertainty quantification over event modeling will help in better decision making for many practical applications. Therefore, we propose a novel point process model, Bayesian Neural Hawkes process (BNHP) which leverages uncertainty modelling capability of Bayesian models and generalization capability of the neural networks to model event occurrence times. We augment the model with spatio-temporal modeling capability where it can consider uncertainty over predicted time and location of the events. Experiments on simulated and real-world datasets show that BNHP significantly improves prediction performance and uncertainty quantification for modelling events.