Uncertainty on Asynchronous Time Event Prediction

This page is about our paper "Uncertainty on Asynchronous TIme Event Prediction" by Marin Biloš, Bertrand Charpentier and Stephan Günnemann, published at the 33rd Conference on Neural Information Processing Systems (Spotlight, NeurIPS), 2019.

Abstract

Asynchronous event sequences are the basis of many applications throughout different industries. In this work, we tackle the task of predicting the next event (given a history), and how this prediction changes with the passage of time. Since at some time points (e.g. predictions far into the future) we might not be able to predict anything with confidence, capturing uncertainty in the predictions is crucial. We present two new architectures, WGP-LN and FD-Dir, modelling the evolution of the distribution on the probability simplex with time-dependent logistic normal and Dirichlet distributions. In both cases, the combination of RNNs with either Gaussian process or function decomposition allows to express rich temporal evolution of the distribution parameters, and naturally captures uncertainty. Experiments on class prediction, time prediction and anomaly detection demonstrate the high performances of our models on various datasets compared to other approaches.

NeurIPS 2019

Come and see us at NeurIPS 2019! We are presenting our work on

Spotlight

Tue, Dec 10th 4:30 -- 4:35 PM
West Ballroom C

Poster

Wed, Dec 11th 10:45 -- 12:45 PM
East Exhibition Hall B + C
Poster #53

Links

[Paper | GitHub | Poster]