Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.
@article{arxiv.2002.09575,
title = {A Multi-Channel Neural Graphical Event Model with Negative Evidence},
author = {Tian Gao and Dharmashankar Subramanian and Karthikeyan Shanmugam and Debarun Bhattacharjya and Nicholas Mattei},
journal= {arXiv preprint arXiv:2002.09575},
year = {2020}
}