Bayesian Neural Hawkes Process for Event Uncertainty Prediction
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 28 of its 79 references that are in the map. First posted on arXiv 29 Dec 2021.
Builds on
28 of 79 references.
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16 more
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